Robust and Efficient RGB-D SLAM in Dynamic Environments

Simultaneous localization and mapping (SLAM) using an RGB-D camera is a key enabling technique for many augmented reality (AR) applications. However, most existing RGB-D SLAM methods could fail in dynamic scenarios due to non-trivial pose estimation errors arising from moving objects. In this study, we present an accurate and robust RGB-D SLAM system for dynamic scenarios which can run real-time on a single dual-core CPU. The core of our system is a robust and efficient dynamic keypoint exclusion method which consists of three steps: 1) grouping spatially and appearance related pixels of a keyframe into regions; 2) identifying dynamic regions by checking motion consistency of keypoints in every region; 3) excluding keypoints in the identified dynamic regions as well as the matching points in the 3D local map. The dynamic keypoint exclusion method can be easily integrated into any keypoint based RGB-D SLAM system for improving the accuracy and robustness in dynamic scenes with trivial time increase (16.6ms per frame). Experimental results on the TUM dataset demonstrates that our method which runs on an Intel i7-4900 CPU is even 2.3X faster than the state-of-the-art method DS-SLAM [1] which runs parallel on a P4000 GPU and a comparable CPU. In addition, our system outperforms the state-of-the-art methods [1]-[4] in terms of smaller absolute trajectory errors (ATE). We also apply our system to a real AR application and live experiments with a hand-held RGB-D camera demonstrate the robustness and generalizability of our method in practical scenarios.(1) (1) A demo video is provided on https://github.com/cc-qy/Dynamic-RGB-D-SLAM

Automated summary

This study introduces an accurate and robust RGB-D SLAM system designed for dynamic scenarios, which runs efficiently on a single dual-core CPU. The system utilizes a dynamic keypoint exclusion method to improve accuracy and robustness in dynamic scenes without significant increase in processing time. Experiment results show that the method outperforms state-of-the-art methods in terms of speed and accuracy, demonstrating its practical applicability in real AR scenarios.