DB-TSDF: Directional Bitmask-based Truncated Signed Distance Fields for Efficient Volumetric Mapping

Abstract-- This paper presents a high-efficiency, CPU-only volumetric mapping framework based on a Truncated Signed Distance Field (TSDF). A key feature of the approach is that the processing time per point-cloud remains constant, regardless of the voxel grid resolution, enabling high resolution mapping without sacrificing runtime performance. In contrast to most recent TSDF/ESDF methods that rely on GPU acceleration, our method operates entirely on CPU, achieving competitive results in speed. Experiments on real-world open datasets demonstrate that the generated maps attain accuracy on par with contemporary mapping techniques. V olumetric mapping is a fundamental capability in mobile robotics, supporting tasks such as collision avoidance, motion planning, and the construction of consistent world models under real-time constraints. Point clouds and occupancy grids remain widely used on CPU-only platforms, as their simple data structures allow efficient processing without specialized hardware. However, they are prone to aliasing at high resolutions and often produce geometric artifacts that hinder downstream processing.