Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

激光里程计问题 #91

Open
YWL0720 opened this issue Jan 12, 2024 · 1 comment
Open

激光里程计问题 #91

YWL0720 opened this issue Jan 12, 2024 · 1 comment

Comments

@YWL0720
Copy link

YWL0720 commented Jan 12, 2024

@zfc-zfc 您好!感谢优秀的工作。 我按照您在 #19 中的提示, 测试激光里程计时。发现对于机械式雷达,FAST-LO表现的都很好,但针对于固态雷达如mid-360或avia,效果则变得很不稳定,经常出现抖动的现象。为了排除我自己数据集的影响,我也下载了您上传的mid360_100ms_delay.bag进行了测试。

使用的参数如下。

common:
    lid_topic:  "/livox/lidar"
    imu_topic:  "/livox/imu/async"

preprocess:
    lidar_type: 1                # Livox series LiDAR
    feature_extract_en: false
    scan_line: 6
    blind: 1

initialization:
    cut_frame_num: 1            # must be positive integer
    orig_odom_freq: 10           # original Lidar frequency
    mean_acc_norm: 1         # 1 for livox built-in IMU
    online_refine_time: 20     # seconds
    data_accum_length: 9999999
    Rot_LI_cov: [ 0.00005, 0.00005, 0.00005 ]
    Trans_LI_cov: [ 0.00001, 0.00001, 0.00001 ]

mapping:
    filter_size_surf: 0.05
    filter_size_map: 0.15
    gyr_cov: 0.5
    acc_cov: 0.5
    b_acc_cov: 0.0001
    b_gyr_cov: 0.0001
    det_range: 100.0

publish:
    path_en:  true
    scan_publish_en:  true       # false: close all the point cloud output
    dense_publish_en: true       # false: low down the points number in a global-frame point clouds scan.
    scan_bodyframe_pub_en: false  # true: output the point cloud scans in IMU-body-frame

pcd_save:
    pcd_save_en: false
    interval: -1                 # how many LiDAR frames saved in each pcd file;
                                 # -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames.

得到的效果如图所示
Screenshot from 2024-01-12 22-11-29

我尝试将参数cut_frame_num 改为 5,效果则变得正常。

所以想请问下,造成这样现象的原因是什么呢?如果我想使用不分帧的点云获得稳定的估计,您有什么调整的意见么?

感谢!

@zfc-zfc
Copy link
Collaborator

zfc-zfc commented Jan 13, 2024

@zfc-zfc 您好!感谢优秀的工作。 我按照您在 #19 中的提示, 测试激光里程计时。发现对于机械式雷达,FAST-LO表现的都很好,但针对于固态雷达如mid-360或avia,效果则变得很不稳定,经常出现抖动的现象。为了排除我自己数据集的影响,我也下载了您上传的mid360_100ms_delay.bag进行了测试。

使用的参数如下。

common:
    lid_topic:  "/livox/lidar"
    imu_topic:  "/livox/imu/async"

preprocess:
    lidar_type: 1                # Livox series LiDAR
    feature_extract_en: false
    scan_line: 6
    blind: 1

initialization:
    cut_frame_num: 1            # must be positive integer
    orig_odom_freq: 10           # original Lidar frequency
    mean_acc_norm: 1         # 1 for livox built-in IMU
    online_refine_time: 20     # seconds
    data_accum_length: 9999999
    Rot_LI_cov: [ 0.00005, 0.00005, 0.00005 ]
    Trans_LI_cov: [ 0.00001, 0.00001, 0.00001 ]

mapping:
    filter_size_surf: 0.05
    filter_size_map: 0.15
    gyr_cov: 0.5
    acc_cov: 0.5
    b_acc_cov: 0.0001
    b_gyr_cov: 0.0001
    det_range: 100.0

publish:
    path_en:  true
    scan_publish_en:  true       # false: close all the point cloud output
    dense_publish_en: true       # false: low down the points number in a global-frame point clouds scan.
    scan_bodyframe_pub_en: false  # true: output the point cloud scans in IMU-body-frame

pcd_save:
    pcd_save_en: false
    interval: -1                 # how many LiDAR frames saved in each pcd file;
                                 # -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames.

得到的效果如图所示 Screenshot from 2024-01-12 22-11-29

我尝试将参数cut_frame_num 改为 5,效果则变得正常。

所以想请问下,造成这样现象的原因是什么呢?如果我想使用不分帧的点云获得稳定的估计,您有什么调整的意见么?

感谢!
原理上讲,如果不分帧,无论是机械式雷达还是固态激光雷达 在运动较剧烈(如较快旋转)都是很难保持鲁棒的。原因就是FAST-LO的匀速模型在点云频率较低时变得不合理,而频率高时 两帧间隔时间短,匀速假设更加合理。而至于提到的机械雷达FAST-LO效果好,只是因为#19中提到的几个数据都是在小车上采集的,运动很平缓;而你下载的我提供的数据是手持采集的,且有较快速的旋转。你想不分帧获得稳定的估计,要么用LIO,要么试试其他LO算法。

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants