Using Detection, Tracking and Prediction in Visual SLAM to Achieve Real-time Semantic Mapping of Dynamic Scenarios
This work addresses the challenge of enabling robots to interact in dynamic environments with efficient, real-time semantic mapping, though it is incremental as it builds on ORB-SLAM2.
The paper tackles the problem of real-time semantic mapping in dynamic scenarios by proposing RDS-SLAM, a lightweight system based on ORB-SLAM2 that achieves pose estimation and object-level semantic mapping using only an Intel Core i7 CPU, with results showing it runs at 30.3 ms per frame and achieves comparable accuracy to state-of-the-art systems requiring GPUs.
In this paper, we propose a lightweight system, RDS-SLAM, based on ORB-SLAM2, which can accurately estimate poses and build semantic maps at object level for dynamic scenarios in real time using only one commonly used Intel Core i7 CPU. In RDS-SLAM, three major improvements, as well as major architectural modifications, are proposed to overcome the limitations of ORB-SLAM2. Firstly, it adopts a lightweight object detection neural network in key frames. Secondly, an efficient tracking and prediction mechanism is embedded into the system to remove the feature points belonging to movable objects in all incoming frames. Thirdly, a semantic octree map is built by probabilistic fusion of detection and tracking results, which enables a robot to maintain a semantic description at object level for potential interactions in dynamic scenarios. We evaluate RDS-SLAM in TUM RGB-D dataset, and experimental results show that RDS-SLAM can run with 30.3 ms per frame in dynamic scenarios using only an Intel Core i7 CPU, and achieves comparable accuracy compared with the state-of-the-art SLAM systems which heavily rely on both Intel Core i7 CPUs and powerful GPUs.