intel realsense d435i相机标定中文文档

这篇具有很好参考价值的文章主要介绍了intel realsense d435i相机标定中文文档。希望对大家有所帮助。如果存在错误或未考虑完全的地方,请大家不吝赐教,您也可以点击"举报违法"按钮提交疑问。

intel realsense d435i相机标定中文文档

  • 此文档参考了官方的英文文档,原地址面向英特尔®实感™深度摄像头的 IMU 校准工具 (intelrealsense.com)

  • IMU概述:惯性测量单元(imu)通常由加速度计组成,加速度通常以国际系统(SI)的米/秒为单位输出平方(m/s^2)和陀螺仪,陀螺仪通常以SI单位测量角速度弧度/秒(rad/s)。英特尔RealSense™深度相机中的IMU D435i和D455相机和英特尔®RealSense™激光雷达相机L515没有什么不同和包含加速度计和陀螺仪可配置输出频率。

  • IMU校准参数:IMU标定参数包括内部参数和外部参数。虽然有许多可能的IMU校准参数,为了简单起见我们考虑一下以下参数:

    • 对于加速度计:

      • 比例因子(灵敏度)——这是用来乘以原始测量值的术语,以确保输出是公制尺度。数学上写为英特尔d435i使用手册,计算机视觉,目标检测

      • 偏差(零偏移)-当传感器应该读取零时,用于消除任何非零值的术语。数学上写为英特尔d435i使用手册,计算机视觉,目标检测

      • 离轴项-这些项用于纠正如果加速度计的轴不是正交的。数学上写为英特尔d435i使用手册,计算机视觉,目标检测

    • 对于陀螺仪:

    • 偏差(零偏移)-当传感器应该读取零时,用于取消任何非零值的术语。数学上写为英特尔d435i使用手册,计算机视觉,目标检测

内在参数试图通过对传感器的不准确性进行建模,将原始传感器数据转换为实际测量值。加速度计的数学变换如下:

英特尔d435i使用手册,计算机视觉,目标检测

在这种情况下,陀螺仪遵循以下简单的模型:
英特尔d435i使用手册,计算机视觉,目标检测

有关IMU ininic参数的基础知识,请参阅意法半导体(STMicroelectronics):pdf地址

外部参数包括:

  • Rotation -从左红外(IR)相机(IR1)到IMU的旋转,指定为3 × 3旋转矩阵
  • Translation -从左侧IR相机到IMU的平移量,指定为3 × 1矢量,单位为毫米

本文件的范围仅包括内部参数校准过程。外部参数可用libaresense。

IMU校准工具

在Intel®RealSense™SDK 2.0中包含了一个校准python脚本rs-imu-calibration.py。请稍后在本文档中查看详细信息。以D435i、D455和L515摄像机为例,介绍了IMU的标定过程。

局限性和准确性

虽然所提供的工具取得了良好的整体校准结果,但如果不遵循所有步骤,则校准将不准确。下列因素亦可能影响校正效果:

  • 校准需要将相机设备定位在6个指定的方向和对齐(水平或垂直取决于方向)。更好的对齐最小化误差并提高精度。建议使用3轴水平,以确保最接近重力。

  • 该工具优化了所有方向,虽然平均加速度结果接近重力加速度(9.8 m/s^2),但有时单个方向的加速度可能略低于或超过完美重力加速度。

用户自定义校准

如果用户更喜欢使用自己的工具校准设备以满足特定的应用需求,结果可以按照rs-imu- calibration .py中演示的方法写入设备。

IMU校准表格式请参见pdf表格

表的格式在所有设备上都是相同的,除了表头中的version和table type字段。

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-q2LwDV23-1680153837913)(C:\Users\shogoki\AppData\Roaming\Typora\typora-user-images\image-20230330113004310.png)]

FW 要求

  • 支持IMU校准需要Intel®RealSense™LiDAR Camera L515 FW 1.4.1.0及以上版本。

  • Intel®RealSense™深度相机D435i/D455 FW随相机发布,稍后支持IMU校准。

安装

本节介绍运行校准Python脚本校准设备所需的硬件和软件设置。

硬件

所需硬件包括待校准的D435i、D455或L515设备、一根USB线缆、一台运行Windows 10、Ubuntu 16.04和Ubuntu 18.04的计算机。

  • 设备

    • 使用如下所示的Intel®RealSense™深度相机D435i, Intel®RealSense™深度相机D455,或Intel®RealSense™LiDAR相机L515设备来显示校准过程。英特尔d435i使用手册,计算机视觉,目标检测
  • USB

    • USB-typeC数据线,用于连接设备和主机。
  • PC

    • Windows 10 or ubuntu

软件:在主机上安装以下软件

  • Python On Windows: Download and install Python: https://www.python.org/downloads/windows/
  • Python校准脚本:校准脚本(Intel®RealSense™SDK的一部分)rs-imu-calibration.py,可在https://github.com/IntelRealSense/librealsense上获得。请下载最新版本,以确保支持所有校准功能。该文件位于源代码树的tools/rs-imu-calibration目录中。
#rs-imu-calibration.py
#!/usr/bin/python
from __future__ import print_function
import numpy as np
import sys
import json
import ctypes
import os
import binascii
import struct
import pyrealsense2 as rs
import ctypes
import time
import enum
import threading

# L515
READ_TABLE = 0x43  # READ_TABLE 0x243 0
WRITE_TABLE = 0x44  # WRITE_TABLE 0 <table>

# L515 minimum firmware version required to support IMU calibration
L515_FW_VER_REQUIRED = '01.04.01.00'

is_data = None
get_key = None
if os.name == 'posix':
    import select
    import tty
    import termios

    is_data = lambda: select.select([sys.stdin], [], [], 0) == ([sys.stdin], [], [])
    get_key = lambda: sys.stdin.read(1)

elif os.name == 'nt':
    import msvcrt

    is_data = msvcrt.kbhit
    get_key = lambda: msvcrt.getch()

else:
    raise Exception('Unsupported OS: %s' % os.name)

if sys.version_info[0] < 3:
    input = raw_input

max_float = struct.unpack('f', b'\xff\xff\xff\xff')[0]
max_int = struct.unpack('i', b'\xff\xff\xff\xff')[0]
max_uint8 = struct.unpack('B', b'\xff')[0]

g = 9.80665  # SI Gravity page 52 of https://nvlpubs.nist.gov/nistpubs/Legacy/SP/nistspecialpublication330e2008.pdf

COLOR_RED = "\033[1;31m"
COLOR_BLUE = "\033[1;34m"
COLOR_CYAN = "\033[1;36m"
COLOR_GREEN = "\033[0;32m"
COLOR_RESET = "\033[0;0m"
COLOR_BOLD = "\033[;1m"
COLOR_REVERSE = "\033[;7m"


def int_to_bytes(num, length=4, order='big'):
    res = bytearray(length)
    for i in range(length):
        res[i] = num & 0xff
        num >>= 8
    if num:
        raise OverflowError("Number {} doesn't fit into {} bytes.".format(num, length))
    if order == 'little':
        res.reverse()
    return res


def bytes_to_uint(bytes_array, order='little'):
    bytes_array = list(bytes_array)
    bytes_array.reverse()
    if order == 'little':
        return struct.unpack('>i', struct.pack('BBBB', *([0] * (4 - len(bytes_array))) + bytes_array))[0] & 0xffffffff
    else:
        return struct.unpack('>i', struct.pack('BBBB', *([0] * (4 - len(bytes_array))) + bytes_array))[0] & 0xffffffff


class imu_wrapper:
    class Status(enum.Enum):
        idle = 0,
        rotate = 1,
        wait_to_stable = 2,
        collect_data = 3

    def __init__(self):
        self.pipeline = None
        self.imu_sensor = None
        self.status = self.Status(
            self.Status.idle)  # 0 - idle, 1 - rotate to position, 2 - wait to stable, 3 - pick data
        self.thread = threading.Condition()
        self.step_start_time = time.time()
        self.time_to_stable = 3
        self.time_to_collect = 2
        self.samples_to_collect = 1000
        self.rotating_threshold = 0.1
        self.moving_threshold_factor = 0.1
        self.collected_data_gyro = []
        self.collected_data_accel = []
        self.callback_lock = threading.Lock()
        self.max_norm = np.linalg.norm(np.array([0.5, 0.5, 0.5]))
        self.line_length = 20
        self.is_done = False
        self.is_data = False

    def escape_handler(self):
        self.thread.acquire()
        self.status = self.Status.idle
        self.is_done = True
        self.thread.notify()
        self.thread.release()
        sys.exit(-1)

    def imu_callback(self, frame):
        if not self.is_data:
            self.is_data = True

        with self.callback_lock:
            try:
                if is_data():
                    c = get_key()
                    if c == '\x1b':  # x1b is ESC
                        self.escape_handler()

                if self.status == self.Status.idle:
                    return
                pr = frame.get_profile()
                data = frame.as_motion_frame().get_motion_data()
                data_np = np.array([data.x, data.y, data.z])
                elapsed_time = time.time() - self.step_start_time

                ## Status.collect_data
                if self.status == self.Status.collect_data:
                    sys.stdout.write('\r %15s' % self.status)
                    part_done = len(self.collected_data_accel) / float(self.samples_to_collect)
                    # sys.stdout.write(': %-3.1f (secs)' % (self.time_to_collect - elapsed_time))

                    color = COLOR_GREEN
                    if pr.stream_type() == rs.stream.gyro:
                        self.collected_data_gyro.append(np.append(frame.get_timestamp(), data_np))
                        is_moving = any(abs(data_np) > self.rotating_threshold)
                    else:
                        is_in_norm = np.linalg.norm(data_np - self.crnt_bucket) < self.max_norm
                        if is_in_norm:
                            self.collected_data_accel.append(np.append(frame.get_timestamp(), data_np))
                        else:
                            color = COLOR_RED
                        is_moving = abs(np.linalg.norm(data_np) - g) / g > self.moving_threshold_factor

                        sys.stdout.write(color)
                        sys.stdout.write('[' + '.' * int(part_done * self.line_length) + ' ' * int(
                            (1 - part_done) * self.line_length) + ']')
                        sys.stdout.write(COLOR_RESET)

                    if is_moving:
                        print('WARNING: MOVING')
                        self.status = self.Status.rotate
                        return

                    # if elapsed_time > self.time_to_collect:
                    if part_done >= 1:
                        self.status = self.Status.collect_data
                        sys.stdout.write('\n\nDirection data collected.')
                        self.thread.acquire()
                        self.status = self.Status.idle
                        self.thread.notify()
                        self.thread.release()
                        return

                if pr.stream_type() == rs.stream.gyro:
                    return
                sys.stdout.write('\r %15s' % self.status)
                crnt_dir = np.array(data_np) / np.linalg.norm(data_np)
                crnt_diff = self.crnt_direction - crnt_dir
                is_in_norm = np.linalg.norm(data_np - self.crnt_bucket) < self.max_norm

                ## Status.rotate
                if self.status == self.Status.rotate:
                    sys.stdout.write(': %35s' % (np.array2string(crnt_diff, precision=4, suppress_small=True)))
                    sys.stdout.write(': %35s' % (np.array2string(abs(crnt_diff) < 0.1)))
                    if is_in_norm:
                        self.status = self.Status.wait_to_stable
                        sys.stdout.write('\r' + ' ' * 90)
                        self.step_start_time = time.time()
                        return

                ## Status.wait_to_stable
                if self.status == self.Status.wait_to_stable:
                    sys.stdout.write(': %-3.1f (secs)' % (self.time_to_stable - elapsed_time))
                    if not is_in_norm:
                        self.status = self.Status.rotate
                        return
                    if elapsed_time > self.time_to_stable:
                        self.collected_data_gyro = []
                        self.collected_data_accel = []
                        self.status = self.Status.collect_data
                        self.step_start_time = time.time()
                        return
                return
            except Exception as e:
                print('ERROR?' + str(e))
                self.thread.acquire()
                self.status = self.Status.idle
                self.thread.notify()
                self.thread.release()

    def get_measurements(self, buckets, bucket_labels):
        measurements = []
        print('-------------------------')
        print('*** Press ESC to Quit ***')
        print('-------------------------')
        for bucket, bucket_label in zip(buckets, bucket_labels):
            self.crnt_bucket = np.array(bucket)
            self.crnt_direction = np.array(bucket) / np.linalg.norm(np.array(bucket))
            print('\nAlign to direction: ', self.crnt_direction, ' ', bucket_label)
            self.status = self.Status.rotate
            self.thread.acquire()
            while (not self.is_done and self.status != self.Status.idle):
                self.thread.wait(3)
                if not self.is_data:
                    raise Exception('No IMU data. Check connectivity.')
            if self.is_done:
                raise Exception('User Abort.')
            measurements.append(np.array(self.collected_data_accel))
        return np.array(measurements), np.array(self.collected_data_gyro)

    def enable_imu_device(self, serial_no):
        self.pipeline = rs.pipeline()
        cfg = rs.config()
        cfg.enable_device(serial_no)
        try:
            self.pipeline.start(cfg)
        except Exception as e:
            print('ERROR: ', str(e))
            return False

        # self.sync_imu_by_this_stream = rs.stream.any
        active_imu_profiles = []

        active_profiles = dict()
        self.imu_sensor = None
        for sensor in self.pipeline.get_active_profile().get_device().sensors:
            for pr in sensor.get_stream_profiles():
                if pr.stream_type() == rs.stream.gyro and pr.format() == rs.format.motion_xyz32f:
                    active_profiles[pr.stream_type()] = pr
                    self.imu_sensor = sensor
                if pr.stream_type() == rs.stream.accel and pr.format() == rs.format.motion_xyz32f:
                    active_profiles[pr.stream_type()] = pr
                    self.imu_sensor = sensor
            if self.imu_sensor:
                break
        if not self.imu_sensor:
            print('No IMU sensor found.')
            return False
        print('\n'.join(['FOUND %s with fps=%s' % (str(ap[0]).split('.')[1].upper(), ap[1].fps()) for ap in
                         active_profiles.items()]))
        active_imu_profiles = list(active_profiles.values())
        if len(active_imu_profiles) < 2:
            print('Not all IMU streams found.')
            return False
        self.imu_sensor.stop()
        self.imu_sensor.close()
        self.imu_sensor.open(active_imu_profiles)
        self.imu_start_loop_time = time.time()
        self.imu_sensor.start(self.imu_callback)

        # Make the device use the original IMU values and not already calibrated:
        if self.imu_sensor.supports(rs.option.enable_motion_correction):
            self.imu_sensor.set_option(rs.option.enable_motion_correction, 0)
        return True


class CHeader:
    def __init__(self, version, table_type):
        self.buffer = np.ones(16, dtype=np.uint8) * 255
        self.buffer[0] = int(version[0], 16)
        self.buffer[1] = int(version[1], 16)
        self.buffer.dtype = np.uint16
        self.buffer[1] = int(table_type, 16)

    def size(self):
        return 16

    def set_data_size(self, size):
        self.buffer.dtype = np.uint32
        self.buffer[1] = size

    def set_crc32(self, crc32):
        self.buffer.dtype = np.uint32
        self.buffer[3] = crc32 % (1 << 32)  # convert from signed to unsigned 32 bit

    def get_buffer(self):
        self.buffer.dtype = np.uint8
        return self.buffer


def bitwise_int_to_float(ival):
    return struct.unpack('f', struct.pack('i', ival))[0]


def bitwise_float_to_int(fval):
    return struct.unpack('i', struct.pack('f', fval))[0]


def parse_buffer(buffer):
    cmd_size = 24
    header_size = 16

    buffer.dtype = np.uint32
    tab1_size = buffer[3]
    buffer.dtype = np.uint8
    print('tab1_size (all_data): ', tab1_size)

    tab1 = buffer[cmd_size:cmd_size + tab1_size]  # 520 == epprom++
    tab1.dtype = np.uint32
    tab2_size = tab1[1]
    tab1.dtype = np.uint8
    print('tab2_size (calibration_table): ', tab2_size)

    tab2 = tab1[header_size:header_size + tab2_size]  # calibration table
    tab2.dtype = np.uint32
    tab3_size = tab2[1]
    tab2.dtype = np.uint8
    print('tab3_size (calibration_table): ', tab3_size)

    tab3 = tab2[header_size:header_size + tab3_size]  # D435 IMU Calib Table
    tab3.dtype = np.uint32
    tab4_size = tab3[1]
    tab3.dtype = np.uint8
    print('tab4_size (D435_IMU_Calib_Table): ', tab4_size)

    tab4 = tab3[header_size:header_size + tab4_size]  # calibration data
    return tab1, tab2, tab3, tab4


def get_IMU_Calib_Table(X, product_line):
    version = ['0x02', '0x01']
    table_type = '0x20'

    if product_line == 'L500':
        version = ['0x05', '0x01']
        table_type = '0x243'

    header = CHeader(version, table_type)

    header_size = header.size()
    data_size = 37 * 4 + 96
    size_of_buffer = header_size + data_size  # according to table "D435 IMU Calib Table" here: https://user-images.githubusercontent.com/6958867/50902974-20507500-1425-11e9-8ca5-8bd2ac2d0ea1.png
    assert (size_of_buffer % 4 == 0)
    buffer = np.ones(size_of_buffer, dtype=np.uint8) * 255

    use_extrinsics = False
    use_intrinsics = True

    data_buffer = np.ones(data_size, dtype=np.uint8) * 255
    data_buffer.dtype = np.float32

    data_buffer[0] = bitwise_int_to_float(np.int32(int(use_intrinsics)) << 8 |
                                          np.int32(int(use_extrinsics)))

    intrinsic_vector = np.zeros(24, dtype=np.float32)
    intrinsic_vector[:9] = X[:3, :3].T.flatten()
    intrinsic_vector[9:12] = X[:3, 3]
    intrinsic_vector[12:21] = X[3:, :3].flatten()
    intrinsic_vector[21:24] = X[3:, 3]

    data_buffer[13:13 + X.size] = intrinsic_vector
    data_buffer.dtype = np.uint8

    header.set_data_size(data_size)

    header.set_crc32(binascii.crc32(data_buffer))
    buffer[:header_size] = header.get_buffer()
    buffer[header_size:] = data_buffer
    return buffer


def get_calibration_table(d435_imu_calib_table):
    version = ['0x02', '0x00']
    table_type = '0x20'

    header = CHeader(version, table_type)

    d435_imu_calib_table_size = d435_imu_calib_table.size
    sn_table_size = 32
    data_size = d435_imu_calib_table_size + sn_table_size

    header_size = header.size()
    size_of_buffer = header_size + data_size  # according to table "D435 IMU Calib Table" in "https://sharepoint.ger.ith.intel.com/sites/3D_project/Shared%20Documents/Arch/D400/FW/D435i_IMU_Calibration_eeprom_0_52.xlsx"
    assert (size_of_buffer % 4 == 0)
    buffer = np.ones(size_of_buffer, dtype=np.uint8) * 255

    data_buffer = np.ones(data_size, dtype=np.uint8) * 255
    data_buffer[:d435_imu_calib_table_size] = d435_imu_calib_table

    header.set_data_size(data_size)
    header.set_crc32(binascii.crc32(data_buffer))

    buffer[:header_size] = header.get_buffer()
    buffer[header_size:header_size + data_size] = data_buffer
    return buffer


def get_eeprom(calibration_table):
    version = ['0x01', '0x01']
    table_type = '0x09'

    header = CHeader(version, table_type)

    DC_MM_EEPROM_SIZE = 520
    # data_size = calibration_table.size

    header_size = header.size()
    size_of_buffer = DC_MM_EEPROM_SIZE
    data_size = size_of_buffer - header_size
    # size_of_buffer = header_size + data_size

    assert (size_of_buffer % 4 == 0)
    buffer = np.ones(size_of_buffer, dtype=np.uint8) * 255

    header.set_data_size(data_size)
    buffer[header_size:header_size + calibration_table.size] = calibration_table
    header.set_crc32(binascii.crc32(buffer[header_size:]))

    buffer[:header_size] = header.get_buffer()

    return buffer


def write_eeprom_to_camera(eeprom, serial_no=''):
    # DC_MM_EEPROM_SIZE = 520
    DC_MM_EEPROM_SIZE = eeprom.size
    DS5_CMD_LENGTH = 24

    MMEW_Cmd_bytes = b'\x14\x00\xab\xcd\x50\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'

    buffer = np.ones([DC_MM_EEPROM_SIZE + DS5_CMD_LENGTH, ], dtype=np.uint8) * 255
    cmd = np.array(struct.unpack('I' * 6, MMEW_Cmd_bytes), dtype=np.uint32)
    cmd.dtype = np.uint16
    cmd[0] += DC_MM_EEPROM_SIZE
    cmd.dtype = np.uint32
    cmd[3] = DC_MM_EEPROM_SIZE  # command 1 = 0x50
    # command 2 = 0
    # command 3 = size
    cmd.dtype = np.uint8
    buffer[:len(cmd)] = cmd
    buffer[len(cmd):len(cmd) + eeprom.size] = eeprom

    debug = get_debug_device(serial_no)
    if not debug:
        print('Error getting RealSense Device.')
        return
    # tab1, tab2, tab3, tab4 = parse_buffer(buffer)

    rcvBuf = debug.send_and_receive_raw_data(bytearray(buffer))
    if rcvBuf[0] == buffer[4]:
        print('SUCCESS: saved calibration to camera.')
    else:
        print('FAILED: failed to save calibration to camera.')
        print(rcvBuf)


def get_debug_device(serial_no):
    ctx = rs.context()
    devices = ctx.query_devices()
    found_dev = False
    for dev in devices:
        if len(serial_no) == 0 or serial_no == dev.get_info(rs.camera_info.serial_number):
            found_dev = True
            break
    if not found_dev:
        print('No RealSense device found' + str('.' if len(serial_no) == 0 else ' with serial number: ' + serial_no))
        return 0

    # print(a few basic information about the device)
    print('  Device PID: ', dev.get_info(rs.camera_info.product_id))
    print('  Device name: ', dev.get_info(rs.camera_info.name))
    print('  Serial number: ', dev.get_info(rs.camera_info.serial_number))
    print('  Firmware version: ', dev.get_info(rs.camera_info.firmware_version))
    debug = rs.debug_protocol(dev)
    return debug


def check_X(X, accel, show_graph):
    fdata = np.apply_along_axis(np.dot, 1, accel, X[:3, :3]) - X[3, :]
    norm_data = (accel ** 2).sum(axis=1) ** (1. / 2)
    norm_fdata = (fdata ** 2).sum(axis=1) ** (1. / 2)
    if show_graph:
        import pylab
        pylab.plot(norm_data, '.b')
        # pylab.hold(True)
        pylab.plot(norm_fdata, '.g')
        pylab.show()
    print('norm (raw data  ): %f' % np.mean(norm_data))
    print('norm (fixed data): %f' % np.mean(norm_fdata), "A good calibration will be near %f" % g)


def l500_send_command(dev, op_code, param1=0, param2=0, param3=0, param4=0, data=[], retries=1):
    for i in range(retries):
        try:
            debug_device = rs.debug_protocol(dev)
            gvd_command_length = 0x14 + len(data)
            magic_number1 = 0xab
            magic_number2 = 0xcd

            buf = bytearray()
            buf += bytes(int_to_bytes(gvd_command_length, 2))
            # buf += bytes(int_to_bytes(0, 1))
            buf += bytes(int_to_bytes(magic_number1, 1))
            buf += bytes(int_to_bytes(magic_number2, 1))
            buf += bytes(int_to_bytes(op_code))
            buf += bytes(int_to_bytes(param1))
            buf += bytes(int_to_bytes(param2))
            buf += bytes(int_to_bytes(param3))
            buf += bytes(int_to_bytes(param4))
            buf += bytearray(data)
            l = list(buf)
            res = debug_device.send_and_receive_raw_data(buf)

            if res[0] == op_code:
                res1 = res[4:]
                return res1
            else:
                raise Exception("send_command return error", res[0])
        except:
            if i < retries - 1:
                time.sleep(0.1)
            else:
                raise


def wait_for_rs_device(serial_no):
    ctx = rs.context()

    start = int(round(time.time() * 1000))
    now = int(round(time.time() * 1000))

    while now - start < 5000:
        devices = ctx.query_devices()
        for dev in devices:
            pid = str(dev.get_info(rs.camera_info.product_id))
            if len(serial_no) == 0 or serial_no == dev.get_info(rs.camera_info.serial_number):
                # print(a few basic information about the device)
                print('  Device PID: ', dev.get_info(rs.camera_info.product_id))
                print('  Device name: ', dev.get_info(rs.camera_info.name))
                print('  Serial number: ', dev.get_info(rs.camera_info.serial_number))
                print('  Product Line: ', dev.get_info(rs.camera_info.product_line))
                print('  Firmware version: ', dev.get_info(rs.camera_info.firmware_version))

                return dev
        time.sleep(5)
        now = int(round(time.time() * 1000))
    raise Exception('No RealSense device' + str('.' if len(serial_no) == 0 else ' with serial number: ' + serial_no))


def main():
    if any([help_str in sys.argv for help_str in ['-h', '--help', '/?']]):
        print("Usage:", sys.argv[0], "[Options]")
        print
        print('[Options]:')
        print('-i : /path/to/accel.txt [/path/to/gyro.txt]')
        print('-s : serial number of device to calibrate.')
        print('-g : show graph of norm values - original values in blue and corrected in green.')
        print
        print('If -i option is given, calibration is done using previosly saved files')
        print('Otherwise, an interactive process is followed.')
        sys.exit(1)

    try:
        accel_file = None
        gyro_file = None
        serial_no = ''
        show_graph = '-g' in sys.argv
        for idx in range(len(sys.argv)):
            if sys.argv[idx] == '-i':
                accel_file = sys.argv[idx + 1]
                if len(sys.argv) > idx + 2 and not sys.argv[idx + 2].startswith('-'):
                    gyro_file = sys.argv[idx + 2]
            if sys.argv[idx] == '-s':
                serial_no = sys.argv[idx + 1]

        print('waiting for realsense device...')

        dev = wait_for_rs_device(serial_no)

        product_line = dev.get_info(rs.camera_info.product_line)

        if product_line == 'L500':
            print('checking minimum firmware requirement ...')
            fw_version = dev.get_info(rs.camera_info.firmware_version)
            if fw_version < L515_FW_VER_REQUIRED:
                raise Exception(
                    'L515 requires firmware ' + L515_FW_VER_REQUIRED + " or later to support IMU calibration. Please upgrade firmware and try again.")
            else:
                print('  firmware ' + fw_version + ' passed check.')

        buckets = [[0, -g, 0], [g, 0, 0],
                   [0, g, 0], [-g, 0, 0],
                   [0, 0, -g], [0, 0, g]]

        # all D400 and L500 cameras with IMU equipped with a mounting screw at the bottom of the device
        # when device is in normal use position upright facing out, mount screw is pointing down, aligned with positive Y direction in depth coordinate system
        # IMU output on each of these devices is transformed into the depth coordinate system, i.e.,
        # looking from back of the camera towards front, the positive x-axis points to the right, the positive y-axis points down, and the positive z-axis points forward.
        # output of motion data is consistent with convention that positive direction aligned with gravity leads to -1g and opposite direction leads to +1g, for example,
        # positive z_aixs points forward away from front glass of the device,
        #  1) if place the device flat on a table, facing up, positive z-axis points up, z-axis acceleration is around +1g
        #  2) facing down, positive z-axis points down, z-axis accleration would be around -1g
        #
        buckets_labels = ["Mounting screw pointing down, device facing out",
                          "Mounting screw pointing left, device facing out",
                          "Mounting screw pointing up, device facing out",
                          "Mounting screw pointing right, device facing out", "Viewing direction facing down",
                          "Viewing direction facing up"]

        gyro_bais = np.zeros(3, np.float32)
        old_settings = None
        if accel_file:
            if gyro_file:
                # compute gyro bais

                # assume the first 4 seconds the device is still
                gyro = np.loadtxt(gyro_file, delimiter=",")
                gyro = gyro[gyro[:, 0] < gyro[0, 0] + 4000, :]

                gyro_bais = np.mean(gyro[:, 1:], axis=0)
                print(gyro_bais)

            # compute accel intrinsic parameters
            max_norm = np.linalg.norm(np.array([0.5, 0.5, 0.5]))

            measurements = [[], [], [], [], [], []]
            import csv
            with open(accel_file, 'r') as csvfile:
                reader = csv.reader(csvfile)
                rnum = 0
                for row in reader:
                    M = np.array([float(row[1]), float(row[2]), float(row[3])])
                    is_ok = False
                    for i in range(0, len(buckets)):
                        if np.linalg.norm(M - buckets[i]) < max_norm:
                            is_ok = True
                            measurements[i].append(M)
                    rnum += 1
            print('read %d rows.' % rnum)
        else:
            print('Start interactive mode:')
            if os.name == 'posix':
                old_settings = termios.tcgetattr(sys.stdin)
                tty.setcbreak(sys.stdin.fileno())

            imu = imu_wrapper()
            if not imu.enable_imu_device(serial_no):
                print('Failed to enable device.')
                return -1
            measurements, gyro = imu.get_measurements(buckets, buckets_labels)
            con_mm = np.concatenate(measurements)
            if os.name == 'posix':
                termios.tcsetattr(sys.stdin, termios.TCSADRAIN, old_settings)

            header = input(
                '\nWould you like to save the raw data? Enter footer for saving files (accel_<footer>.txt and gyro_<footer>.txt)\nEnter nothing to not save raw data to disk. >')
            print('\n')
            if header:
                accel_file = 'accel_%s.txt' % header
                gyro_file = 'gyro_%s.txt' % header
                print('Writing files:\n%s\n%s' % (accel_file, gyro_file))
                np.savetxt(accel_file, con_mm, delimiter=',', fmt='%s')
                np.savetxt(gyro_file, gyro, delimiter=',', fmt='%s')
            else:
                print('Not writing to files.')
            # remove times from measurements:
            measurements = [mm[:, 1:] for mm in measurements]

            gyro_bais = np.mean(gyro[:, 1:], axis=0)
            print(gyro_bais)

        mlen = np.array([len(meas) for meas in measurements])
        print(mlen)
        print('using %d measurements.' % mlen.sum())

        nrows = mlen.sum()
        w = np.zeros([nrows, 4])
        Y = np.zeros([nrows, 3])
        row = 0
        for i in range(0, len(buckets)):
            for m in measurements[i]:
                w[row, 0] = m[0]
                w[row, 1] = m[1]
                w[row, 2] = m[2]
                w[row, 3] = -1
                Y[row, 0] = buckets[i][0]
                Y[row, 1] = buckets[i][1]
                Y[row, 2] = buckets[i][2]
                row += 1
        np_version = [int(x) for x in np.version.version.split('.')]
        rcond_val = None if (np_version[1] >= 14 or np_version[0] > 1) else -1
        X, residuals, rank, singular = np.linalg.lstsq(w, Y, rcond=rcond_val)

        print(X)
        print("residuals:", residuals)
        print("rank:", rank)
        print("singular:", singular)
        check_X(X, w[:, :3], show_graph)

        calibration = {}

        if product_line == 'L500':
            calibration["device_type"] = "L515"
        else:
            calibration["device_type"] = "D435i"

        calibration["imus"] = list()
        calibration["imus"].append({})
        calibration["imus"][0]["accelerometer"] = {}
        calibration["imus"][0]["accelerometer"]["scale_and_alignment"] = X.flatten()[:9].tolist()
        calibration["imus"][0]["accelerometer"]["bias"] = X.flatten()[9:].tolist()
        calibration["imus"][0]["gyroscope"] = {}
        calibration["imus"][0]["gyroscope"]["scale_and_alignment"] = np.eye(3).flatten().tolist()
        calibration["imus"][0]["gyroscope"]["bias"] = gyro_bais.tolist()
        json_data = json.dumps(calibration, indent=4, sort_keys=True)

        directory = os.path.dirname(accel_file) if accel_file else '.'

        with open(os.path.join(directory, "calibration.json"), 'w') as outfile:
            outfile.write(json_data)

        # concatinate the two 12 element arrays and save
        intrinsic_buffer = np.zeros([6, 4])

        intrinsic_buffer[:3, :4] = X.T
        intrinsic_buffer[3:, :3] = np.eye(3)
        intrinsic_buffer[3:, 3] = gyro_bais

        # intrinsic_buffer = ((np.array(range(24),np.float32)+1)/10).reshape([6,4])

        imu_calib_table = get_IMU_Calib_Table(intrinsic_buffer, product_line)

        with open(os.path.join(directory, "calibration.bin"), 'wb') as outfile:
            outfile.write(imu_calib_table.astype('f').tostring())

        is_write = input('Would you like to write the results to the camera? (Y/N)')
        is_write = 'Y' in is_write.upper()
        if is_write:
            print('Writing calibration to device.')

            if product_line == 'L500':
                l500_send_command(dev, WRITE_TABLE, 0, 0, 0, 0, imu_calib_table)
            else:
                calibration_table = get_calibration_table(imu_calib_table)
                eeprom = get_eeprom(calibration_table)
                write_eeprom_to_camera(eeprom, serial_no)

            print('Done.')
        else:
            print('Abort writing to device')

    except Exception as e:
        print('\nError: %s' % e)
    finally:
        if os.name == 'posix' and old_settings is not None:
            termios.tcsetattr(sys.stdin, termios.TCSADRAIN, old_settings)

    """
    wtw = dot(transpose(w),w)
    wtwi = np.linalg.inv(wtw)
    print(wtwi)
    X = dot(wtwi, Y)
    print(X)
    """


if __name__ == '__main__':
    main()
  • Intel® RealSense™ SDK 2.0:安装最新版本的英特尔®RealSense™SDK。表3-1包含了指向SDK主页、GitHub*仓库(可以下载最新版本)和SDK文档的指针。英特尔d435i使用手册,计算机视觉,目标检测

  • Pip / Numpy / Enum

  • pyrealsense2(python库)

校准装置与Python校准脚本

  • 过程概述

    使用Python校准脚本校准设备的一般过程启动脚本以捕获6个不同位置的IMU数据,然后计算参数并将结果写入相机。在执行校准过程之前,阅读整个章节非常重要。英特尔d435i使用手册,计算机视觉,目标检测

    记录数据---------->计算校准---------->将校准写入设备

  • 将设备连接到计算机

    使用USB线将设备连接到已安装Intel®RealSense™SDK 2.0的PC上。

  • 运行rs-imu-calibration.py文件

    • 启动进程

      • 在Ubuntu中使用bash终端或在Windows中使用命令提示符找到rs-imu-calibration.py的安装位置。
        在命令提示符中:python rs-imu-calibration.py

      • 脚本开始时的示例输出:

        waiting for realsense device…

        Device PID: 0B64

        Device name: Intel RealSense L515

        Serial number: 00000000f9340895

        Product Line: L500 Firmware version: 01.04.01.00

        checking minimum firmware requirement …

        firmware 01.04.01.00 passed check.

        Start interactive mode:

        FOUND GYRO with fps=100 FOUND ACCEL with fps=100

        ***Press ESC to Quit ***

      • 注意:在L515上,脚本将检查FW以确保它满足最低要求的FW 1.4.1.0。如果没有,它会停止并打印出一条警告消息,要求用户升级固件。

  • 从6个位置捕获IMU数据

    • 校准Python脚本中的校准算法需要设备的6个不同位置来计算校准。设备应该在每一个位置保持3到4秒。一定要在每个位置保持相机尽可能稳定。

    • 由于产品设计和IMU在产品中的物理配置,D435i, D455和L515之间的位置不同,但在每个位置的目标是使IMU的轴与重力方向对齐,并按以下顺序描述。这些产品在设备的底部都有¼-20螺纹的三脚架安装螺纹,所以下面的校准脚本和说明将其作为设备物理定位的辅助。

    • 使用真实的照片来更好地说明准确的位置和方向。

    • D435i and D455

      • 根据校准脚本的指示,在以下位置用D435i设备捕获数据。D455的物理形状和USB端口位置略有不同,但总体校准程序相似。

      • 位置#1 -安装螺钉指向下方,设备朝外英特尔d435i使用手册,计算机视觉,目标检测

        • 开始录制后,如上所述,保持相机在这个位置不动3到4秒。该脚本将为每个位置提供提示和指导。

        • 输出示例英特尔d435i使用手册,计算机视觉,目标检测

      • 位置2 -安装螺钉向左,设备朝外:摄像机朝向上节所述的相同方向,围绕摄像机的观看方向旋转摄像机90度,使¼- 20螺纹三脚架指向左侧。英特尔d435i使用手册,计算机视觉,目标检测

        • 保持相机在这个位置不动3到4秒。英特尔d435i使用手册,计算机视觉,目标检测
      • 位置3 -安装螺钉指向上,设备朝向外

        • 从2号位置旋转相机一个额外的90度关于相机的观察方向,这样现在相机是倒置的¼- 20螺纹三脚架架面对上。英特尔d435i使用手册,计算机视觉,目标检测

        • 保持相机在这个位置不动3到4秒。

      • 位置4 -安装螺钉指向右侧,设备朝外

        • 从3号位置旋转相机一个额外的90度,使¼- 20螺纹三脚架安装指向右边。英特尔d435i使用手册,计算机视觉,目标检测

        • 保持相机在这个位置不动3到4秒。

      • 姿势5 -观察方向朝下

        • 将相机的观看方向朝下,使英特尔®RealSense™标志朝上。英特尔d435i使用手册,计算机视觉,目标检测

        • 保持相机在这个位置不动3到4秒。

      • 姿势6 -观察方向朝上

        • 从5号位置将摄像头围绕USB线旋转180度,使英特尔®RealSense™标志朝下。英特尔d435i使用手册,计算机视觉,目标检测

        • 保持相机在这个位置不动3到4秒。让相机保持在这个最后的位置,直到录音停止。

  • 计算校准

    • 捕获所有6个位置后,脚本将提供一个提示,询问是否要保存原始数据。英特尔d435i使用手册,计算机视觉,目标检测
  • 将结果更新到设备文章来源地址https://www.toymoban.com/news/detail-709139.html

    • 计算校准后,脚本提供了将结果写入相机的eeprom的选项。

到了这里,关于intel realsense d435i相机标定中文文档的文章就介绍完了。如果您还想了解更多内容,请在右上角搜索TOY模板网以前的文章或继续浏览下面的相关文章,希望大家以后多多支持TOY模板网!

本文来自互联网用户投稿,该文观点仅代表作者本人,不代表本站立场。本站仅提供信息存储空间服务,不拥有所有权,不承担相关法律责任。如若转载,请注明出处: 如若内容造成侵权/违法违规/事实不符,请点击违法举报进行投诉反馈,一经查实,立即删除!

领支付宝红包 赞助服务器费用

相关文章

  • d435i 相机和imu标定

    使用 imu_utils 功能包标定 IMU,由于imu_utils功能包的编译依赖于code_utils,需要先编译code_utils,主要参考 相机与IMU联合标定_熊猫飞天的博客-CSDN博客 Ubuntu20.04编译并运行imu_utils,并且标定IMU_学无止境的小龟的博客-CSDN博客 1.1 编译 code_utils 创建工作空间 1.1.1 修改 CMakeLists.txt 文件

    2024年02月09日
    浏览(43)
  • realsense D435i 实现外部时钟触发硬件同步多相机数据采集

    最近有一个调试D435i相机的工作,需要使得三个相机能够完成硬件触发的同步,具体来说,就是有一个固定频率的外部脉冲信号,使得三个相机能够根据外部脉冲信号的硬件触发完成双目图片、深度图片、彩色图片、IMU数据的实时响应采集,因为外部脉冲信号是通过一个精确

    2024年01月16日
    浏览(32)
  • 项目设计:YOLOv5目标检测+机构光相机(intel d455和d435i)测距

    1.1  Intel D455 Intel D455 是一款基于结构光(Structured Light)技术的深度相机。 与ToF相机不同,结构光相机使用另一种方法来获取物体的深度信息。它通过投射可视光谱中的红外结构光图案,然后从被拍摄物体表面反射回来的图案重建出其三维形状和深度信息。 Intel D455 深度相机

    2024年02月08日
    浏览(36)
  • ubuntu18.04安装Realsense D435i相机SDK及realsense-ros记录,为后期运行yolo v5作准备

    写在前面 :一定要注意各个版本之间的匹配问题,否则会报各种错误。 例如ROS版本和librealsense SDK版本之间的对应关系,以及realsense-ros(Wrapper)与librealsense SDK之间的对应关系 。 系统:ubuntu18.04 ros: melodic 附上Intel® RealSense github网站: https://github.com/IntelRealSense 以及安装教程

    2024年02月05日
    浏览(40)
  • Realsense d435i驱动安装、配置及校准

    写在前面 本文是在ubuntu20.04下安装,其它版本大同小异。可能出现的问题,主要由各自安装相关库版本不一致导致,故问题不一,但一般很好解决,正常情况下不会出现。 Intel Realsense 深度摄像头D435i将D435强大的深度感知能力和惯性测量单元(IMU)结合起来,可满足RGBD、单目、

    2024年02月02日
    浏览(36)
  • Realsense D435i Yolov5目标检测实时获得目标三维位置信息

    - Colorimage: - Colorimage and depthimage: 1.一个可以运行YOLOv5的python环境 2.一个realsense相机和pyrealsense2库 在下面两个环境中测试成功 win10 python 3.8 Pytorch 1.10.2+gpu CUDA 11.3 NVIDIA GeForce MX150 ubuntu16.04 python 3.6 Pytorch 1.7.1+cpu 修改模型配置文件,以yolov5s为例。 如果使用自己训练的模型,需要进

    2024年02月04日
    浏览(42)
  • 【深度相机D435i】Windows+Ubuntu下调用D435i利用Python读取、保存RGB、Depth图片

    最近组里面的项目需要用到D435i深度相机采集深度图片,所以记录一下在Windows+Ubuntu的环境下使用D435i深度相机的流程,以及如何利用python读取、保存常见的RGB、Depth图片。 D435i 在小巧外形中采用英特尔模块和视觉处理器,是一个功能强大的一体产品,可与可定制软件配合使用

    2024年02月02日
    浏览(27)
  • 使用D435i相机录制TUM格式的数据集

    本文写于2023年3月14日。 D435i相机的rgb图像与depth图像的像素没有对齐,在此记录一下如何像素对齐 Ubuntu18.04 + ROS melodic 这一步需要使用 InterRealSenseD435i SDK2 ,可以参考此链接安装。 打开 Intel RealSense Viewer 。设置 Depth Stream 以及 Color Stream 的图像分辨率为 640 × 480 ,设置采集帧率

    2024年02月09日
    浏览(33)
  • python实现d435i深度相机测量两点之间的距离

    本文介绍python方法实现intel公司realsense系列d435i深度相机测量彩色图像上两点之间的距离。 原理很简单,就是将相机获得的彩色图像流与深度流对齐,这样彩色图像上的每个像素就会对应一个深度值,作为z坐标,然后通过相机内参获得该像素的x坐标和y坐标。我们获得的x、

    2024年02月16日
    浏览(26)
  • 【D435i深度相机YOLO V5结合实现目标检测】

    参考:Ubutntu下使用realsense d435i(三):使用yolo v5测量目标物中心点三维坐标 欢迎大家阅读2345VOR的博客【D435i深度相机YOLO V5结合实现目标检测】🥳🥳🥳 2345VOR鹏鹏主页: 已获得CSDN《嵌入式领域优质创作者》称号👻👻👻,座右铭:脚踏实地,仰望星空🛹🛹🛹 本文章属于

    2024年02月08日
    浏览(44)

觉得文章有用就打赏一下文章作者

支付宝扫一扫打赏

博客赞助

微信扫一扫打赏

请作者喝杯咖啡吧~博客赞助

支付宝扫一扫领取红包,优惠每天领

二维码1

领取红包

二维码2

领红包