Source code for the article "GroundGrid: LiDAR Point Cloud Ground Segmentation and Terrain Estimation"
This repository contains the source code for the article "GroundGrid: LiDAR Point Cloud Ground Segmentation and Terrain Estimation" published in the IEEE Robotics and Automation Letters (DOI: 10.1109/LRA.2023.3333233).
- ROS Noetic Ninjemys
- catkin
- roscpp
- geometry_msgs
- sensor_msgs
- std_msgs
- message_generation
- message_runtime
- velodyne_pointcloud
- nodelet
- dynamic_reconfigure
- grid_map_core
- grid_map_ros
- grid_map_cv
- grid_map_loader
- grid_map_msgs
- grid_map_rviz_plugin
- grid_map_visualization
- cv_bridge
- pcl_ros
catkin build -DCMAKE_BUILD_TYPE=Release groundgrid
roslaunch groundgrid KITTIPlayback.launch directory:=/path/to/the/SemanticKITTI/dataset sequence:=0
The launch file opens a RViz window which displays the segmentation results:
roslaunch groundgrid KITTIEvaluate.launch directory:=/path/to/the/SemanticKITTI/dataset sequence:=0
This launch file evaluates the ground segmentation performance of GroundGrid and displays the results every 500 processed clouds. The final results are displayed upon receiving Ctrl+C in the terminal:
Stats
Received 4540 point clouds. KITTI sequence 00.
label nonground % ground % nonground total
unlabeled 88.74% 11.26% 6512861 7339364
outlier 42.51% 57.49% 121616 286056
car 94.42% 5.58% 43078880 45622648
bicycle 89.85% 10.15% 200919 223610
motorcycle 95.39% 4.61% 500522 524684
truck 97.61% 2.39% 416124 426308
other-vehicle 96.34% 3.66% 1199946 1245564
person 95.95% 4.05% 68227 71104
bicyclist 100.00% 0.00% 5 5
motorcyclist 0.00% 100.00% 0 8
road 0.07% 99.93% 68465 95649669
parking 0.45% 99.55% 37828 8450594
sidewalk 0.91% 99.09% 716154 78601664
other-ground 6.43% 93.57% 192 2985
building 97.33% 2.67% 117586234 120810401
fence 88.91% 11.09% 15821127 17793867
other-structure 89.92% 10.08% 713791 793778
lane-marking 0.16% 99.84% 171 109456
vegetation 93.43% 6.57% 121595505 130139604
trunk 97.88% 2.12% 4495649 4592878
terrain 6.68% 93.32% 1939107 29038187
pole 98.14% 1.86% 1819715 1854290
traffic-sign 99.87% 0.13% 248254 248565
other-object 89.59% 10.41% 4577201 5109077
moving-car 96.48% 3.52% 245225 254183
moving-bicyclist 98.23% 1.77% 155263 158054
moving-person 96.51% 3.49% 85024 88099
moving-motorcyclist 94.68% 5.32% 2011 2124
moving-bus 98.26% 1.74% 902 918
moving-other-vehicle 94.24% 5.76% 34090 36175
Precision 96.05% 209090638 8607231
Recall 98.70% 209090638 2761917
F1 97.35% 8607231 2761917
Accuracy 97.24% 400339747 411708895
IoUg 94.84%
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