Update README.md

Author: qdLMF

README.md

@@ -1,9 +1,9 @@
 # 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](https://github.com/gaoxiang12/faster-lio])) on GPU is used to accelerate 5-neighbour KNN search.
 
-The codes of this reimplementation are in [src/cuda_plane_line_odometry](https://github.com/qdLMF/LIO-SAM-CUDA-ScanToMapOpt/tree/master/src/cuda_plane_line_odometry). 
-
-The scan2MapOptimization() is replaced with scan2MapOptimizationWithCUDA().
+Modifications are as follow : 
+- The CUDA codes of the line/plane odometry are in [src/cuda_plane_line_odometry](https://github.com/qdLMF/LIO-SAM-CUDA-ScanToMapOpt/tree/master/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 cloud hash map (inspired by iVox of [Faster-LIO](https://github.com/gaoxiang12/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 one frame is down to about 11ms. Meanwhile, other parts of the line/plane odometry (jacobians & residuals etc) are also implemented with CUDA.