• cmu中间代码groud_based_autonomy_basic在适配不同机器人时主要修改的参数

    １．根据使用的slam算法，loam_interface需要修改launch,　具体如何修改其官方给出了文档，

    1. If running on a real robot, launch state estimation module and the system side by side. Use the
    2. command below to launch the system.
    3. roslaunch vehicle_simulator system_real_robot.launch
    4. If playing bagfiles, make sure to set 'use_sim_time = true'. In a terminal,
    5. roscore
    6. In another terminal,
    7. rosparam set use_sim_time true
    8. After launching the state estimation module and the system, play bagfiles with '--clock' flag
    9. (change 'filename' in the command line),
    10. rosbag play --clock filename.bag
    11. Adjust 'obstacleHeightThre' in 'src/local_planner/launch/local_planner.launch' to account for
    12. terrain cloud thickness.
    13. LOAM
    14. Notes: Use 'loam_interface' package as is to bridge over the state estimation output.
    15. Code: https://github.com/cuitaixiang/LOAM_NOTED
    16. Reference: J. Zhang and S. Singh. LOAM: Lidar Odometry and Mapping in Real-time. Robotics:
    17. Science and Systems Conference (RSS). Berkeley, CA, July 2014.
    18. A-LOAM (lidar only without IMU)
    19. Notes: In 'src/loam_interface/launch/loam_interface.launch', set 'stateEstimationTopic =
    20. /aft_mapped_to_init_high_frec', 'flipStateEstimation = false', and 'flipRegisteredScan = false'.
    21. Code: https://github.com/HKUST-Aerial-Robotics/A-LOAM
    22. Reference: N/A
    23. LeGO-LOAM
    24. Notes: In 'src/loam_interface/launch/loam_interface.launch', set 'registeredScanTopic =
    25. /registered_cloud'.
    26. Code: https://github.com/RobustFieldAutonomyLab/LeGO-LOAM
    27. Reference: T. Shan and B. Englot. LeGO-LOAM: Lightweight and Ground-Optimized Lidar
    28. Odometry and Mapping on Variable Terrain. IEEE/RSJ Intl. Conf. on Intelligent Robots and
    29. Systems (IROS). Madrid, Spain, Oct. 2018.LIO-SAM
    30. Notes: In 'src/loam_interface/launch/loam_interface.launch', set 'stateEstimationTopic =
    31. /lio_sam/mapping/odometry', 'registeredScanTopic = /lio_sam/mapping/cloud_registered',
    32. 'flipStateEstimation = false', and 'flipRegisteredScan = false'.
    33. Code: https://github.com/TixiaoShan/LIO-SAM
    34. Reference: T. Shan, B. Englot, D. Meyers, W. Wang, C. Ratti, and D. Rus. LIO-SAM: Tightly-
    35. coupled Lidar Inertial Odometry via Smoothing and Mapping. IEEE/RSJ Intl. Conf. on Intelligent
    36. Robots and Systems (IROS). Las Vegas, Nevada, Oct. 2020.
    37. LIO-mapping
    38. Notes: In 'src/loam_interface/launch/loam_interface.launch', set 'stateEstimationTopic =
    39. /lio_map_builder/aft_mapped_to_init', 'registeredScanTopic =
    40. /lio_map_builder/cloud_registered', 'flipStateEstimation = false', and 'flipRegisteredScan =
    41. false'.
    42. Code: https://github.com/hyye/lio-mapping
    43. Reference: H. Ye, Y. Chen, and M. Liu. Tightly Coupled 3D Lidar Inertial Odometry and
    44. Mapping. IEEE Intl. Conf. on Robotics and Automation (ICRA). Montreal, Canada, May 2019.
    45. FAST-LIO2
    46. Notes: In 'src/loam_interface/launch/loam_interface.launch', set 'stateEstimationTopic =
    47. /Odometry', 'registeredScanTopic = /cloud_registered', 'flipStateEstimation = false', and
    48. 'flipRegisteredScan = false'.
    49. Code: https://github.com/hku-mars/FAST_LIO
    50. Reference: W. Xu, Y. Cai, D. He, J. Lin, and F. Zhang. FAST-LIO2: Fast Direct LiDAR-Inertial
    51. Odometry. IEEE Transactions on Robotics. vol. 38, no. 4, pp. 2053–2073, 2022.
    52. Faster-LIO
    53. Notes: In 'src/loam_interface/launch/loam_interface.launch', set 'stateEstimationTopic =
    54. /Odometry', 'registeredScanTopic = /cloud_registered', 'flipStateEstimation = false', and
    55. 'flipRegisteredScan = false'.
    56. Code: https://github.com/gaoxiang12/faster-lio
    57. Reference: C. Bai, T. Xiao, Y. Chen, H. Wang, F. Zhang, and X. Gao. Faster-LIO: Lightweight
    58. Tightly Coupled Lidar-Inertial Odometry Using Parallel Sparse Incremental Voxels. IEEE Robotics
    59. and Automation Letters. vol. 7, no. 2, pp. 4861–4868, 2022.

    2.local_planner

    local_planner.launch中

    　false－只能前进去目标点

    　最大线速度

    　自动模式

    　自动运动时的线速度

    type="string" value="$(find local_planner)/paths" />这个是黄色的局部曲线，根据机器人的外接圆进行修改，如机器人为一个长方体，长Ｌ，宽w，则path_generator.m中的　Radius >= sqrt(pow(L/2,2) + pow(W/2,2))，可以稍微大一点．

    宽度可以稍微大个0.1，这个会计算一个半径，因为差速底盘尤其四轮差速有一个中情况就是转向时，障碍物在其侧面，如果点云被包含在这个半径内且在侧面，会对路径作出调整

    在室内结构化平坦场地时不使用地形分析的点云使用slam配准后的scan_registered也可以，不过最好使用地形分析的

    对应差速度底盘设置为true,,因为其旋转时四差底盘可能碰撞

    局部路径的探测范围，就是那个黄色线的范围

    点云相对高度大于这个值才认为是障碍物点云点，但是高度是一个相对高度，所以根据实际情况设置

    这个在考虑地形分析点云或者点云点高度大于障碍物阈值时，对路径进行惩罚，当点云点高度大于地面高度时这个路径才会被惩罚，根据实际工况进行调试

    /*********************************************************************************************/

    local_planner.launch中对path_follow.cpp的配置，这其中有几个比较重要的参数

    首先需要设定的是需要的线速度和最大线速度，这个在最开始设置．然后根据这个设置

    yawRateGain" type="double" value="1" />默认是7.5,

    这个用来增加角速度的，根据线速度调试确定，否则摆动很大前进时

    ，这个同上面类似，是减角速度的，需跟线速度协调

    最大角速度，默认90度，这个也是根线速度协调

    最大加速度，也是与线速度协调，因为会用这个对线速度进行加速和减速

    重要参数，当目标点与机器人之间的角度差阈值大于这个，则减速．

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  • 原文地址：https://blog.csdn.net/dwell548560/article/details/132509003