BITD: geometric-intensity fusion for robust LiDAR place recognition

Purpose – This paper aims to address the limitations of LiDAR-based place recognition in complex and large-scale environments. In practice, performance often degrades due to sparse data, sensor noise, dynamic objects and slow retrieval in large maps. These issues highlight the need for more robust and efficient place recognition approaches. Design/methodology/approach – This study presents the baseline intensity-assisted triangle descriptor, which combines geometric features with calibrated LiDAR intensity for robust place recognition. The process involves intensity calibration, plane detection, image generation and keypoint extraction. Based on these, triangle and intensity descriptors are constructed to represent structure and reflectivity. They are then used in a two-stage loop closure framework for fast retrieval and reliable verification, ensuring both accuracy and efficiency. Finally, the proposed method is thoroughly evaluated on the benchmark data set. Findings – Experimental results indicate that the proposed method achieves higher precision and recall compared to state-of-the-art approaches. On average, the F1-score increases by approximately 26% over STD and 24% over Scan Context. The two-stage framework effectively reduces false matches while maintaining high recall, and the introduction of the baseline intensity descriptor significantly improves retrieval efficiency. Originality/value – This work proposes a lightweight and interpretable global descriptor that integrates geometric structure with calibrated LiDAR intensity. By combining these complementary features in a two-stage detection framework, the method achieves a balance of accuracy, robustness and efficiency.