双目IMU相机

1. 功能介绍

地瓜双目IMU相机自带完整的标定参数，包括双目标定参数、IMU内参、双目和IMU之间的外参，用户无需额外标定即可直接使用。利用这些参数，可以通过双目立体匹配算法计算高精度的深度图，实现实时的三维环境感知。同时，相机的数据能应用于开源视觉惯性里程计（VIO）算法，如 OpenVINS 等，可计算相机位姿与轨迹信息。适用于机器人导航、避障等应用，为开发者提供即插即用的深度感知与视觉惯性融合能力。

mipi相机代码仓库：https://github.com/D-Robotics/hobot_mipi_cam

2. 支持平台

平台	系统支持	示例功能
RDK X5, RDK X5 Module	Ubuntu 22.04 (Humble)	启动双目相机，输出双目图像和IMU数据

3. 准备工作

3.1. RDK平台

RDK已烧录好RDK OS系统
RDK已成功安装TogetheROS.Bot
确认PC机能够通过网络访问RDK

3.2. 系统和功能包版本

	版本	查询方法
RDK X5系统镜像版本	3.4.1及以上	`cat /etc/version`
tros-humble-hobot-stereonet功能包版本	2.5.0及以上	`apt list \| grep tros-humble-hobot-stereonet/`
tros-humble-mipi-cam功能包版本	2.5.0及以上	`apt list \| grep tros-humble-mipi-cam/`

如果系统镜像版本不符合要求，请参考文档对应章节进行镜像烧录
如果功能包版本不符合要求，请执行以下指令进行更新：

sudo apt update
sudo apt install --only-upgrade tros-humble-hobot-stereonet
sudo apt install --only-upgrade tros-humble-mipi-cam

注意

如果sudo apt update命令执行失败或报错，请查看常见问题章节的Q10: apt update 命令执行失败或报错如何处理？解决。

4. 启动双目相机

4.1. 双目IMU相机

注意相机有黑色金属外壳和亚克力外壳两个版本，功能都是一样的

LH_IMU_cam

4.2. 硬件连接

模组背后开关设置为EXT模式，不要在LPWM模式
相机的黑色杜邦线要连接RDK X5的37管脚，需要外部触发控制相机和IMU的时间同步，管脚定义可以查看3.1.1 管脚定义与应用

RDK_X5_LH_IMU_cam

4.3. RDK X5配置

RDK X5上要进行相应的设置才能读取IMU数据，首先检查一下系统版本是否符合要求，需要3.4.1版本以上：

cat /etc/version

在RDK X5上运行以下指令进行配置：

srpi-config

LH_IMU_cam_config1 LH_IMU_cam_config2 LH_IMU_cam_config3 LH_IMU_cam_config4

重启后，确认目录/sys/bus/iio/devices/有iio:device1和iio:device2则代表配置成功

ll /sys/bus/iio/devices/

LH_IMU_cam_config5

4.4. 相机启动指令

执行如下指令启动相机：

source /opt/tros/humble/setup.bash

ros2 launch mipi_cam mipi_cam_dual_channel.launch.py \
mipi_channel:=2 mipi_channel2:=0 \
mipi_lpwm_enable:=True mipi_frame_ts_type:=realtime \
mipi_image_width:=816 mipi_image_height:=960 \
mipi_image_framerate:=10.0 mipi_gdc_enable:=True \
mipi_out_format:=nv12 \
log_level:=info

参数解释：

mipi_channel:=2 mipi_channel2:=0 调整左右目拼接顺序
mipi_lpwm_enable:=True 开启LPWM硬件同步
mipi_frame_ts_type:=realtime 时间戳采用系统时间
mipi_image_width:=816 mipi_image_height:=960 调整图像分辨率，最高可设置为1088*1280
mipi_image_framerate:=10.0 调整相机帧率，最高可调整为30.0
mipi_gdc_enable:=True 开启GDC矫正会发布矫正后的双目图像，否则会发布带畸变的图像
mipi_out_format:=nv12 设置图像格式，支持nv12/bgr8
log_level:=info 日志等级，info等级可以打印标定参数，如果不需要显示那么多信息，则设置为warn

程序运行成功会打印如下日志，包括相机全部的标定参数，目前双目标定采用鱼眼模式，可以参考OpenCV fisheye的介绍：

LH_IMU_cam_run_success_log

发布的话题如下：

ros2 topic list -v

LH_IMU_cam_topic

/image_combine_raw 为上下拼接的双目图像，mipi_channel:=2 mipi_channel2:=0 参数控制拼接顺序
/image_left_raw//image_right_raw 为左右目数据话题，mipi_channel:=2 mipi_channel2:=0 参数控制顺序
/imu_data 为IMU数据话题，发布陀螺仪和加速度计数据

注意

imu数据话题中angular_velocity 单位是 rad/s，linear_acceleration单位是 m/s²，重力加速度值是9.81

5. 启动双目深度算法

5.1. 启动指令

参考双目深度算法，里面有双目算法的相关介绍和启动指令介绍
本相机启动指令为：

bash run_stereo.sh --mipi_rotation 0.0

# 参考对应文档查看具体的参数设置

5.2. 结果展示

启动后，可以在web端查看RGB图和深度图，在浏览器输入 http://ip:8000 (图中RDK IP是192.168.128.10)：

LH_IMU_cam_DStereo

6. 双目VIO算法（以OpenVINS为例）

6.1. 录制rosbag

目前还不支持在线运行VIO算法，需要录制rosbag后在PC上进行计算

运行指令

source /opt/tros/humble/setup.bash

ros2 pkg prefix mipi_cam

ros2 launch mipi_cam mipi_cam_dual_channel.launch.py \
mipi_channel:=2 mipi_channel2:=0 \
mipi_lpwm_enable:=True mipi_frame_ts_type:=realtime \
mipi_image_width:=816 mipi_image_height:=960 \
mipi_image_framerate:=10.0 mipi_gdc_enable:=False \
mipi_out_format:=nv12 \
log_level:=warn

主要录制imu数据话题和双目图像话题，由于RDK X5写入数据的性能有限，不建议录制太长时间

ros2 bag record /imu_data /image_combine_raw --max-cache-size 1073741824

将ros2 bag转为ros1格式，可以自行写程序实现

6.2. 准备VIO参数

配置OpenVINS的参数，在OpenVINS的config目录新建文件夹drobotics_stereo_imu_cam，创建以下3个文件，注意相机的标定文件需要根据上文的命令读取

estimator_config.yaml

%YAML:1.0 # need to specify the file type at the top!

verbosity: "INFO" # ALL, DEBUG, INFO, WARNING, ERROR, SILENT

use_fej: true # if first-estimate Jacobians should be used (enable for good consistency)
integration: "rk4" # discrete, rk4, analytical (if rk4 or analytical used then analytical covariance propagation is used)
use_stereo: true # if we have more than 1 camera, if we should try to track stereo constraints between pairs
max_cameras: 2 # how many cameras we have 1 = mono, 2 = stereo, >2 = binocular (all mono tracking)

calib_cam_extrinsics: true # if the transform between camera and IMU should be optimized R_ItoC, p_CinI
calib_cam_intrinsics: true # if camera intrinsics should be optimized (focal, center, distortion)
calib_cam_timeoffset: true # if timeoffset between camera and IMU should be optimized
calib_imu_intrinsics: false # if imu intrinsics should be calibrated (rotation and skew-scale matrix)
calib_imu_g_sensitivity: false # if gyroscope gravity sensitivity (Tg) should be calibrated

max_clones: 11 # how many clones in the sliding window
max_slam: 50 # number of features in our state vector
max_slam_in_update: 25 # update can be split into sequential updates of batches, how many in a batch
max_msckf_in_update: 40 # how many MSCKF features to use in the update
dt_slam_delay: 1 # delay before initializing (helps with stability from bad initialization...)

gravity_mag: 9.7887 # magnitude of gravity in this location

feat_rep_msckf: "GLOBAL_3D"
feat_rep_slam: "ANCHORED_MSCKF_INVERSE_DEPTH"
feat_rep_aruco: "ANCHORED_MSCKF_INVERSE_DEPTH"

# zero velocity update parameters we can use
# we support either IMU-based or disparity detection.
try_zupt: false
zupt_chi2_multipler: 0 # set to 0 for only disp-based
zupt_max_velocity: 0.1
zupt_noise_multiplier: 10
zupt_max_disparity: 0.5 # set to 0 for only imu-based
zupt_only_at_beginning: false

# ==================================================================
# ==================================================================

init_window_time: 2.0 # how many seconds to collect initialization information
init_imu_thresh: 1.5 # threshold for variance of the accelerometer to detect a "jerk" in motion
init_max_disparity: 10.0 # max disparity to consider the platform stationary (dependent on resolution)
init_max_features: 50 # how many features to track during initialization (saves on computation)

init_dyn_use: false # if dynamic initialization should be used
init_dyn_mle_opt_calib: false # if we should optimize calibration during intialization (not recommended)
init_dyn_mle_max_iter: 50 # how many iterations the MLE refinement should use (zero to skip the MLE)
init_dyn_mle_max_time: 0.05 # how many seconds the MLE should be completed in
init_dyn_mle_max_threads: 6 # how many threads the MLE should use
init_dyn_num_pose: 6 # number of poses to use within our window time (evenly spaced)
init_dyn_min_deg: 10.0 # orientation change needed to try to init

init_dyn_inflation_ori: 10 # what to inflate the recovered q_GtoI covariance by
init_dyn_inflation_vel: 100 # what to inflate the recovered v_IinG covariance by
init_dyn_inflation_bg: 10 # what to inflate the recovered bias_g covariance by
init_dyn_inflation_ba: 100 # what to inflate the recovered bias_a covariance by
init_dyn_min_rec_cond: 1e-12 # reciprocal condition number thresh for info inversion

init_dyn_bias_g: [ 0.0, 0.0, 0.0 ] # initial gyroscope bias guess
init_dyn_bias_a: [ 0.0, 0.0, 0.0 ] # initial accelerometer bias guess

# ==================================================================
# ==================================================================

record_timing_information: false # if we want to record timing information of the method
record_timing_filepath: "/tmp/traj_timing.txt" # https://docs.openvins.com/eval-timing.html#eval-ov-timing-flame

# if we want to save the simulation state and its diagional covariance
# use this with rosrun ov_eval error_simulation
save_total_state: false
filepath_est: "/tmp/ov_estimate.txt"
filepath_std: "/tmp/ov_estimate_std.txt"
filepath_gt: "/tmp/ov_groundtruth.txt"

# ==================================================================
# ==================================================================

# our front-end feature tracking parameters
# we have a KLT and descriptor based (KLT is better implemented...)
use_klt: true # if true we will use KLT, otherwise use a ORB descriptor + robust matching
num_pts: 200 # number of points (per camera) we will extract and try to track
fast_threshold: 20 # threshold for fast extraction (warning: lower threshs can be expensive)
grid_x: 5 # extraction sub-grid count for horizontal direction (uniform tracking)
grid_y: 5 # extraction sub-grid count for vertical direction (uniform tracking)
min_px_dist: 10 # distance between features (features near each other provide less information)
knn_ratio: 0.70 # descriptor knn threshold for the top two descriptor matches
track_frequency: 21.0 # frequency we will perform feature tracking at (in frames per second / hertz)
downsample_cameras: false # will downsample image in half if true
num_opencv_threads: 4 # -1: auto, 0-1: serial, >1: number of threads
histogram_method: "HISTOGRAM" # NONE, HISTOGRAM, CLAHE

# aruco tag tracker for the system
# DICT_6X6_1000 from https://chev.me/arucogen/
use_aruco: false
num_aruco: 1024
downsize_aruco: true

# ==================================================================
# ==================================================================

# camera noises and chi-squared threshold multipliers
up_msckf_sigma_px: 1
up_msckf_chi2_multipler: 1
up_slam_sigma_px: 1
up_slam_chi2_multipler: 1
up_aruco_sigma_px: 1
up_aruco_chi2_multipler: 1

# masks for our images
use_mask: false

# imu and camera spacial-temporal
# imu config should also have the correct noise values
relative_config_imu: "kalibr_imu_chain.yaml"
relative_config_imucam: "kalibr_imucam_chain.yaml"

kalibr_imu_chain.yaml

      
%YAML:1.0

imu0:
  T_i_b:
    - [1.0, 0.0, 0.0, 0.0]
    - [0.0, 1.0, 0.0, 0.0]
    - [0.0, 0.0, 1.0, 0.0]
    - [0.0, 0.0, 0.0, 1.0]
  accelerometer_noise_density: 0.02229489595390929  # [ m / s^2 / sqrt(Hz) ]   ( accel "white noise" )
  accelerometer_random_walk: 0.0001785433950802699  # [ m / s^3 / sqrt(Hz) ].  ( accel bias diffusion )
  gyroscope_noise_density: 0.001145986736669183     # [ rad / s / sqrt(Hz) ]   ( gyro "white noise" )
  gyroscope_random_walk: 1.2431490829218913e-05     # [ rad / s^2 / sqrt(Hz) ] ( gyro bias diffusion ) 
  rostopic: /imu/data
  time_offset: 0.0
  update_rate: 344.0
  # three different modes supported:
  # "calibrated" (same as "kalibr"), "kalibr", "rpng"
  model: "kalibr"
  # how to get from Kalibr imu.yaml result file:
  #   - Tw is imu0:gyroscopes:M:
  #   - R_IMUtoGYRO: is imu0:gyroscopes:C_gyro_i:
  #   - Ta is imu0:accelerometers:M:
  #   - R_IMUtoACC not used by Kalibr
  #   - Tg is imu0:gyroscopes:A:
  Tw:
    - [ 1.0, 0.0, 0.0 ]
    - [ 0.0, 1.0, 0.0 ]
    - [ 0.0, 0.0, 1.0 ]
  R_IMUtoGYRO:
    - [ 1.0, 0.0, 0.0 ]
    - [ 0.0, 1.0, 0.0 ]
    - [ 0.0, 0.0, 1.0 ]
  Ta:
    - [ 1.0, 0.0, 0.0 ]
    - [ 0.0, 1.0, 0.0 ]
    - [ 0.0, 0.0, 1.0 ]
  R_IMUtoACC:
    - [ 1.0, 0.0, 0.0 ]
    - [ 0.0, 1.0, 0.0 ]
    - [ 0.0, 0.0, 1.0 ]
  Tg:
    - [ 0.0, 0.0, 0.0 ]
    - [ 0.0, 0.0, 0.0 ]
    - [ 0.0, 0.0, 0.0 ]

kalibr_imucam_chain.yaml

%YAML:1.0
cam0:
  T_cam_imu:
    - [-0.9999730211612913, 0.0011654864078604154, 0.007252488606936615, 0.05855576022314668]
    - [0.0011859186262073965, 0.9999953385940187, 0.002813607514727948, 0.002858928245212033]
    - [-0.00724917557882737, 0.0028221324681899397, -0.9999697420531085, -0.005490310834812385]
    - [0.0, 0.0, 0.0, 1.0]
  cam_overlaps: [1]
  camera_model: pinhole
  distortion_coeffs: [-0.029225109913193572, 0.02082403492287568, -0.03194158971070967, 0.0165934134408496]
  distortion_model: equidistant
  intrinsics: [497.81111262347383, 497.8055103598601, 396.48820025924294, 463.4451903675188]
  resolution: [816, 960]
  rostopic: /left_camera/image_raw
  timeshift_cam_imu: 0.005901386399303203
cam1:
  T_cam_imu:
    - [-0.9999845146989352, 0.004550487081340542, 0.003203658792316898, -0.010601870574768642]
    - [0.0045684145407885015, 0.9999738226044781, 0.005611033271290525, 0.002251517696825047]
    - [-0.0031780419944595945, 0.00562558202416158, -0.9999791262201281, -0.005008993420470941]
    - [0.0, 0.0, 0.0, 1.0]
  T_cn_cnm1:
    - [0.9999860743077356, 0.0033735794462250975, 0.004058343544320085, -0.06914417862245166]
    - [-0.003362141436934531, 0.9999903663337931, -0.0028219221399547337, -0.00042600348830910853]
    - [-0.0040678244261232255, 0.0028082381177397444, 0.9999877832269312, 0.0007114163409117424]
    - [0.0, 0.0, 0.0, 1.0]
  cam_overlaps: [0]
  camera_model: pinhole
  distortion_coeffs: [-0.028463396839828604, 0.012205852066252196, -0.01306188175421103, 0.001536051968099967]
  distortion_model: equidistant
  intrinsics: [494.7591785888601, 494.9156071869387, 384.67971500453064, 488.34269230328925]
  resolution: [816, 960]
  rostopic: /right_camera/image_raw
  timeshift_cam_imu: 0.00591541095880247

6.3. 启动OpenVINS

开启3个终端，分别执行

# 启动openvins
roslaunch ov_msckf subscribe.launch config:=drobotics_stereo_imu_cam verbosity:=DEBUG \
dosave:=true path_est:=~/openvins_traj.txt

# 启动rviz
rosrun rviz rviz -d <OpenVINS目录>/open_vins/ov_msckf/launch/display.rviz

# 播放采集的rosbag数据
rosbag play xxx_ros1.bag

LH_IMU_cam_OpneVINS

1. 功能介绍​

2. 支持平台​

3. 准备工作​

3.1. RDK平台​

3.2. 系统和功能包版本​

4. 启动双目相机​

4.1. 双目IMU相机​

4.2. 硬件连接​

4.3. RDK X5配置​

4.4. 相机启动指令​

5. 启动双目深度算法​

5.1. 启动指令​

5.2. 结果展示​

6. 双目VIO算法（以OpenVINS为例）​

6.1. 录制rosbag​

6.2. 准备VIO参数​

6.3. 启动OpenVINS​