IMU-Assisted 2D SLAM Method for Low-Texture and Dynamic Environments

Abstract

Generally, the key issues of 2D LiDAR-based simultaneous localization and mapping (SLAM) for indoor application include data association (DA) and closed-loop detection. Particularly, a low-texture environment, which refers to no obvious changes between two consecutive scanning outputs, with moving objects existing in the environment will bring great challenges on DA and the closed-loop detection, and the accuracy and consistency of SLAM may be badly affected. There is not much literature that addresses this issue. In this paper, a mapping strategy is firstly exploited to improve the performance of the 2D SLAM in dynamic environments. Secondly, a fusion method which combines the IMU sensor with a 2D LiDAR, based on framework of extended Kalman Filter (EKF), is proposed to enhance the performance under low-texture environments. In the front-end of the proposed SLAM method, initial motion estimation is obtained from the output of EKF, and it can be taken as the initial pose for the scan matching problem. Then the scan matching problem can be optimized by the Levenberg–Marquardt (LM) algorithm. For the back-end optimization, a sparse pose adjustment (SPA) method is employed. To improve the accuracy, the grid map is updated with the bicubic interpolation method for derivative computing. With the improvements both in the DA process and the back-end optimization stage, the accuracy and consistency of SLAM results in low-texture environments is enhanced. Qualitative and quantitative experiments with open-loop and closed-loop cases have been conducted and the results are analyzed, confirming that the proposed method is effective in low-texture and dynamic indoor environments.

Scan Matching by Cross-Correlation and Differential Evolution

“…The LiDAR periodically measures the distance to the nearest object in the direction of a laser beam it emits [48]. In a 2-dimensional LiDAR, the measuring laser beam is rotated on the horizontal plane and creates a laser range scan that corresponds to the surrounding of the scanner [49]. 3-dimensional LiDARs also tilt the measuring sensor in the vertical direction and obtain 3-dimensional point clouds [50].…”

Special Feature on Advanced Mobile Robotics

“…In low-texture environments, data association and closed-loop detection are challenging problems in the SLAM (simultaneous localization and mapping) method. Wang et al [13] showed that the data association process and the back-end optimization stage with sensors, the IMU (Inertial Measurement Unit) sensor and a 2D LiDAR (Light Detection and Ranging), can be improved to enhance navigation performance. The SLAM algorithm is applied to a non-flat road with a 3D LiDAR sensor by Wang et al [14].…”

Development of a Low Cost and Path-free Autonomous Patrol System Based on Stereo Vision System and Checking Flags

“…A robot with a built-in audio source is used in the research to interact with microphone arrays which are at known coordinates in the environment to locate the robot itself. Wang, Z. et al propose an IMU-Assisted SLAM method to improve the localization accuracy of 2D LiDAR by using extended Kalman filter [27]. An V. et al propose a path planning method that a patrol robot can re-plan a patrol path from sufficient number of observation points while meets dynamic obstacles [28].…”