Parallel OctoMapping: A Scalable Framework for Enhanced Path Planning in Autonomous Navigation

TL;DR

Parallel OctoMapping (POMP) is a multi-threaded, efficient mapping technique that enhances free space representation at fixed resolution, improving path planning success and efficiency in cluttered environments.

Contribution

POMP is the first method to refine free space representation at fixed resolution while maintaining map fidelity and compatibility with existing planners.

Findings

Higher pathfinding success rates in cluttered environments.

Shorter path lengths achieved with POMP.

Significant computational efficiency improvements.

Abstract

Mapping is essential in robotics and autonomous systems because it provides the spatial foundation for path planning. Efficient mapping enables planning algorithms to generate reliable paths while ensuring safety and adapting in real time to complex environments. Fixed-resolution mapping methods often produce overly conservative obstacle representations that lead to suboptimal paths or planning failures in cluttered scenes. To address this issue, we introduce Parallel OctoMapping (POMP), an efficient OctoMap-based mapping technique that maximizes available free space and supports multi-threaded computation. To the best of our knowledge, POMP is the first method that, at a fixed occupancy-grid resolution, refines the representation of free space while preserving map fidelity and compatibility with existing search-based planners. It can therefore be integrated into existing planning pipelines, yielding higher pathfinding success rates and shorter path lengths, especially in cluttered environments, while substantially improving computational efficiency.