To address the limitations of existing terrain mapping methods that ignore temporal misalignment, adopt non-adaptive pixel-grid projection and only provide coarse traversability information, this paper aims to propose Terrain Semantic Map Building Framework (TSM-BF) to construct fine-grained terrain semantic maps for more reliable and intelligent navigation of field robots in complex outdoor environments.
A TSM-BF is proposed, integrating depth camera and 3D LiDAR data for terrain perception. The framework introduces a timestamp cropping mechanism to handle temporal misalignment between semantic and depth image streams, and an adaptive Pixel-Grid Match strategy to achieve accurate semantic projection onto grid maps.
Experimental results demonstrate that the terrain semantic maps generated by TSM-BF provide comprehensive environmental details. The proposed timestamp cropping mechanism and adaptive projection modules effectively improve mapping accuracy and robustness, while maintaining real-time performance for resource-constrained mobile robots.
TSM-BF realizes the integrated design of temporal synchronization and adaptive semantic projection, which overcomes the limitations of coarse-grained traversability representation and sensor temporal misalignment in existing methods. This work provides a practical and deployable terrain semantic mapping solution for autonomous field robots.
