Dynamic scene localization and fusion mapping with a light detection and ranging sensor and a depth camera

Abstract This paper proposes an indoor LiDAR-visual fusion framework based on dynamic elimination. The framework effectively suppresses transient dynamic interferences. It integrates multi-view geometric constraints with semantic priors, which is achieved through a prior model-guided dynamic feature elimination strategy. Moreover, an adaptive gamma correction (AGC) algorithm enhances the image quality under uneven illumination. Meanwhile, an improved quadtree algorithm homogenizes feature point distribution. These two measures achieve more robust ORB feature extraction while ensuring image quality. For map fusion, we focus on combining the processed visual map with the LiDAR map. The visual map, with dynamic feature points eliminated, is first aligned with the LiDAR map via Harris corner detection. We then integrate them using Bayesian inference-based grid information fusion to generate the final integrated map. Extensive evaluations were carried out on six dynamic sequence datasets, and practical dynamic corridor mapping experiments were also conducted. These evaluations and experiments demonstrate the superiority of the proposed system.