LOCUS

LOCUS (Lidar Odometry for Consistent operation in Uncertain Settings) is the lidar odometry solution we deployed on various platforms like the Clearpath Husky and the Boston Dynamic Spot during the DARPA Subterranean Challenge. It is a multi-sensor lidar-centric solution for prevision odometry and 3D mapping in real-time. We open sourced the code along with a few datasets taken from tunnels and caves we tested in when preparing for the challenge.

Please check out our repo.

For more in depth description, see (Reinke et al., 2022).

1. Reinke, A., Palieri, M., Morrell, B., Chang, Y., Ebadi, K., Carlone, L., & Agha-mohammadi, A. (2022). LOCUS 2.0: Robust and Computationally Efficient LiDAR Odometry for Real-Time Underground 3D Mapping. IEEE Robotics and Automation Letters (RA-L), 7(4), 9043–9050.

   @article{Reinke22ral-LOCUS2,
     title = {{LOCUS 2.0}: Robust and Computationally Efficient LiDAR Odometry for Real-Time Underground {3D} Mapping},
     author = {Reinke, A. and Palieri, M. and Morrell, B. and Chang, Y. and Ebadi, K. and Carlone, L. and Agha-mohammadi, A.},
     note = {\linkToPdf{https://arxiv.org/pdf/2205.11784.pdf}},
     journal = {{IEEE} Robotics and Automation Letters ({RA-L})},
     volume = {7},
     number = {4},
     pages = {9043--9050},
     pdf = {https://arxiv.org/pdf/2205.11784.pdf},
     code = {https://github.com/NeBula-Autonomy/LOCUS},
     year = {2022}
   }