SEE-CSOM: Sharp-Edged and Efficient Continous Semantic Occupancy Mapping through Multi-entropy Kernel Inference
This is a novel continuous semantic mapping algorithm, which can complete dense but not thick semantic map reconstruction efficiently.
catkin_ws/src$ git clone https://github.com/BIT-DYN/SEE-CSOM
catkin_ws/src$ cd ..
catkin_ws$ catkin_make
catkin_ws$ source ~/catkin_ws/devel/setup.bash
catkin_ws$ source /opt/intel/compilers_and_libraries/linux/bin/compilervars.sh intel64
catkin_ws$ catkin_make -DCMAKE_C_COMPILER=icc -DCMAKE_CXX_COMPILER=icpc
catkin_ws$ source ~/catkin_ws/devel/setup.bash
$ roslaunch see_csom toy_example_node.launch
$ roslaunch see_csom kitti_node.launch
$ roslaunch see_csom semantickitti_quan.launch
$ roslaunch see_csom stanford_node.launch
@article{deng2023see,
title={SEE-CSOM: Sharp-Edged and Efficient Continuous Semantic Occupancy Mapping for Mobile Robots},
author={Deng, Yinan and Wang, Meiling and Yang, Yi and Wang, Danwei and Yue, Yufeng},
journal={IEEE Transactions on Industrial Electronics},
year={2023},
publisher={IEEE}
}