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

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.

Figures (16)

• Figure 1: System overview of our proposed mapping framework
• Figure 2: Parallel insertion of point clouds into the Octree with concurrent node construction. Each leaf node is subdivided into 4 regions in 2D or 8 regions in 3D. In the figure, " " represents occupied, " " represents unoccupied, and " " denotes the threshold bounding box. The bounding box is centered on a leaf node and used to determine the region state; by default, the edge length of the bounding box is set to half the leaf size. The regions are classified as Clear " " (no points), Safe " ", or Unsafe " " (see Fig.\ref{['fig:trd']}).
• Figure 3: Illustration of the threshold setup and region classification: Clear , Safe, and Unsafe. The left panel depicts the boundary of the area, where $h$ denotes half the edge length of an OctoMap leaf node (equal to the map resolution), and the threshold distance is $\mathrm{thr}=h\cdot\mathrm{ratio}$, with $\mathrm{ratio}$ specified at initialization. Red points lie outside the thresholded boundary, while blue points lie inside; these points determine the region safe state label (unsafe / safe / clear).
• Figure 4: Illustration of the mapping configuration from Octree leaf nodes (left) to leaf regions and their projection onto the OGM (right). The occupancy grid is shown with a dash--dot outline, OctoMap nodes with a long-dashed outline, and region partitions with solid lines.
• Figure 5: Illustration of the diagonal examination pairs and rules for determining grid navigability. is the unsafe region in the leaf node, is the safe region in the leaf node, and is the clear region in the leaf node. The arrow represents the direction of examination. The floating square with a black border indicates that the corresponding cell of OGM should be marked as Occupied after the examination.