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A CUDA reimplementation of the line/plane odometry of LIO-SAM. A point cloud hash map (inspired by iVox of Faster-LIO) on GPU is used to accelerate 5-neighbour KNN search.

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LIO-SAM-GPU-ScanToMapOpt

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().

About

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.

Dependencies

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 :

  • C++14
  • CUDA (>= 11.0)
  • CUBLAS
  • thrust
  • Eigen (>= 3.3.9)

How To Build

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.

Speed-up

SequenceCPU (Intel I7-6700K)GPU (Nvidia 980TI)
build kdtreeone frame
(build kdtree & all iteraions)
build hashmapone KNNone frame
(build hashmap & all iteraions)
Walking16.06ms 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
Park16.11ms 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
Garden17.66ms 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
Rooftop17.48ms 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
Rotation11.01ms 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_002814.33ms 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

Acknowledgements

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.

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A CUDA reimplementation of the line/plane odometry of LIO-SAM. A point cloud hash map (inspired by iVox of Faster-LIO) on GPU is used to accelerate 5-neighbour KNN search.

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