ADS–SLAM: a semantic SLAM based on adaptive motion compensation and semantic information for dynamic environments

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Abstract Static environment assumptions are a prerequisite for simultaneous localization and mapping (SLAM), while interference from dynamic objects in the environment can seriously impair the system’s localization accuracy. Recently, many works have combined deep learning and geometric constraints to attenuate the interference of dynamic objects, but poor real-time performance and low accuracy in high dynamic scenes still exist. In this paper, we propose a semantic SLAM algorithm for complex dynamic scenes named ADS–SLAM. Our system combines the advantages of semantic information and motion constraints to remove dynamic points during tracking and localization. First, an adaptive dynamic point detection method based on epipolar constraint between adjacent frames is designed to adapt to the changes of object motion states and a motion area detection method based on Gaussian mixture model and Kalman Filter is utilized to effectively compensate the missed motion areas. Second, an object detection network with improved inference in the backend is utilized to extract prior object semantics. Lastly, the multi-level information is integrated in order to comprehensively screen all dynamic points in the environment and utilize only static points for pose estimation and optimization. Experimental evaluations on challenging public datasets and outdoor dynamic environments demonstrate that our algorithm achieves high localization accuracy in almost all dynamic scenarios compared to the current state-of-the-art SLAM algorithms, with the highest accuracy in high dynamic scenarios, and shows real-time performance for practical applications.