coVoxSLAM: GPU Accelerated Globally Consistent Dense SLAM

TL;DR

coVoxSLAM is a GPU-accelerated dense SLAM system that achieves fast, globally consistent mapping in large-scale environments, suitable for various platforms and open-source availability.

Contribution

This work introduces coVoxSLAM, a novel GPU-based volumetric SLAM system that significantly improves performance while maintaining accuracy in large-scale environments.

Findings

Significant reduction in execution times compared to state-of-the-art methods.

Maintains accurate localization in large-scale environments.

Compatible with both discrete and embedded GPUs.

Abstract

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 GPU) 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 open-source on GitHub https://github.com/lrse-uba/coVoxSLAM.