This repository reimplements the line/plane odometry (based on LOAM) of LIO-SAM with CUDA. Replacing pcl's kdtree, a point cloud hash map (inspired by iVox of Faster-LIO) on GPU is used to accelerate 5-neighbour KNN search.
Modifications are as follow :
- The CUDA codes of the line/plane odometry are in src/cuda_plane_line_odometry.
- To use this CUDA odometry, the scan2MapOptimization() in mapOptimization.cpp is replaced with scan2MapOptimizationWithCUDA().
This repository reimplements the line/plane odometry in scan2MapOptimization() of mapOptimization.cpp with CUDA. The most significant cost of the original implementation is the 5-neighbour KNN search using pcl's kdtree, which, on my machine (intel i7-6700k CPU, walking_dataset.bag, with OpenMP), usually takes about 5ms. This repository replaces pcl's kdtree with a point cloud hash map (inspired by iVox of Faster-LIO) implemented with CUDA. On my machine (Nvidia 980TI CPU, walking_dataset.bag), average cost of the 5-neighbour KNN search is down to about 0.5~0.6ms, average cost of all operations in one frame is down to about 11ms. Meanwhile, other parts of the line/plane odometry (jacobians & residuals etc) are also implemented with CUDA.
The essential dependencies are as same as LIO-SAM, tested on Ubuntu 18.04 & Ros Melodic.
In addition, the CUDA reimplementation of the line/plane odometry requires :
Before build this repo, some CMAKE variables in src/cuda_plane_line_odometry/CMakeLists.txt need to be modified to fit your enviroment :
set(CMAKE_CUDA_COMPILER /usr/local/cuda/bin/nvcc) # change it to your path to nvcc
set(CUDA_TOOLKIT_ROOT_DIR /usr/local/cuda/bin/nvcc) # change it to your path to nvcc
set(CMAKE_CUDA_ARCHITECTURES 52) # for example, if your device's compute capability is 6.2, then set this CMAKE variable to 62
The basic steps to compile and run this repo is as same as LIO-SAM.
| Sequence | CPU (Intel I7-6700K) | GPU (Nvidia 980TI) | |||
|---|---|---|---|---|---|
| build kdtree | one frame (build kdtree & all iteraions) | build hashmap | one KNN | one frame (build hashmap & all iteraions) |
|
| Walking | 16.06ms no RVIZ 29.00ms with RVIZ | 49.98ms no RVIZ 84.20ms with RVIZ | 4.52ms no RVIZ 6.93ms with RVIZ | 0.57ms no RVIZ 0.58ms with RVIZ | 11.06ms no RVIZ 15.68ms with RVIZ |
| Park | 16.11ms no RVIZ 28.08ms with RVIZ | 59.02ms no RVIZ 101.38ms with RVIZ | 4.18ms no RVIZ 6.71ms with RVIZ | 0.62ms no RVIZ 0.62ms with RVIZ | 11.41ms no RVIZ 16.55ms with RVIZ |
| Garden | 17.66ms no RVIZ 31.71ms with RVIZ | 53.40ms no RVIZ 84.24ms with RVIZ | 5.01ms no RVIZ 7.43ms with RVIZ | 0.60ms no RVIZ 0.61ms with RVIZ | 11.42ms no RVIZ 15.66ms with RVIZ |
| Rooftop | 17.48ms no RVIZ 36.78ms with RVIZ | 67.81ms no RVIZ 120.75ms with RVIZ | 4.96ms no RVIZ 8.30ms with RVIZ | 0.81ms no RVIZ 0.82ms with RVIZ | 13.63ms no RVIZ 19.86ms with RVIZ |
| Rotation | 11.01ms no RVIZ 10.80ms with RVIZ | 50.30ms no RVIZ 53.15ms with RVIZ | 4.01ms no RVIZ 4.40ms with RVIZ | 0.54ms no RVIZ 0.55ms with RVIZ | 9.77ms no RVIZ 10.27ms with RVIZ |
| Campus (small) | 17.88ms no RVIZ 37.30ms with RVIZ | 58.68ms no RVIZ 115.68ms with RVIZ | 4.70ms no RVIZ 7.62ms with RVIZ | 0.60ms no RVIZ 0.62ms with RVIZ | 11.89ms no RVIZ 17.83ms with RVIZ |
| Campus (large) | 16.20ms no RVIZ 28.39ms with RVIZ | 60.67ms no RVIZ 108.08ms with RVIZ | 4.76ms no RVIZ 7.50ms with RVIZ | 0.62ms no RVIZ 0.63ms with RVIZ | 12.48ms no RVIZ 17.47ms with RVIZ |
| 2011_09_30_drive_0028 | 14.33ms no RVIZ 22.25ms with RVIZ | 110.22ms no RVIZ 168.98ms with RVIZ | 5.20ms no RVIZ 7.44ms with RVIZ | 1.05ms no RVIZ 1.05ms with RVIZ | 19.64ms no RVIZ 24.50ms with RVIZ |
This repository is a modified version of LIO-SAM, whose line/plane odometry is originally based upon LOAM.
The point cloud hash map on GPU is inspired by iVox data structure of Faster-LIO, and draws experience from kdtree_cuda_builder.h of FLANN.