coVoxSLAM: GPU Accelerated Globally Consistent Dense SLAM

A dense SLAM system is essential for mobile robots, as it provides localization and allows navigation, path planning, obstacle avoidance, and decision making in unstructured environments. Due to increasing computational demands, the use of GPUs in dense SLAM is expanding. In this work, we present coVoxSLAM, a novel GPU-accelerated volumetric SLAM system that takes full advantage of the parallel processing power of the GPU to build globally consistent maps even in large-scale environments. It was deployed on different platforms (discrete and embedded GPUs) and compared with the state-of-the-art. The results obtained using public datasets show that coVoxSLAM delivers a significant performance improvement considering execution times while maintaining accurate localization. The presented system is available as an open-source system on GitHub.11https://github.com/lrse-uba/coVoxSLAM