A Stereo Visual-Inertial SLAM Approach for Indoor Mobile Robots in Unknown Environments Without Occlusions

When mobile robots are working in indoor unknown environments, the surrounding scenes are mainly low texture or repeating texture. This means that image features are easily lost when tracking the robots, and poses are difficult to estimate as the robot moves back and forth in a narrow area. In order to improve such tracking problems, we propose a one-circle feature-matching method, which refers to a sequence of the circle matching for the time after space (STCM), and an STCM-based visual-inertial simultaneous localization and mapping (STCM-SLAM) technique. This strategy tightly couples the stereo camera and the inertial measurement unit (IMU) in order to better estimate poses of the mobile robot when working indoors. Forward backward optical flow is used to track image features. The absolute accuracy and relative accuracy of STCM increase by 37.869% and 129.167%, respectively, when compared with correlation flow. In addition, we compare our proposed method with other state-of-the-art methods. In terms of relative pose error, the accuracy of STCM-SLAM is an order of magnitude greater than ORB-SLAM2, and two orders of magnitude greater than OKVIS. Our experiments show that STCM-SLAM has obvious advantages over the OKVIS method, specifically in terms of scale error, running frequency, and CPU load. STCM-SLAM also performs the best under real-time conditions. In the indoor experiments, STCM-SLAM is able to accurately estimate the trajectory of the mobile robot. Based on the root mean square error, mean error, and standard deviation, the accuracy of STCM-SLAM is ultimately superior to that of either ORB-SLAM2 or OKVIS.