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.
Highlights
Simultaneous localization and mapping (SLAM) provides the mobile robot the ability to set up a model of the working space and to localize itself, and it is the most important ability for a truly autonomous robot able to operate within real-world environments
LiDAR has the advantages of high precision, good real-time performance, and strong anti-interference ability, so the LiDAR-based simultaneous localization and mapping (SLAM) has been widely used in many practical applications, such as autonomous vehicles, home service robots, and automatic guided vehicles in civilian areas
Sensor, and an initial motion estimation can be obtained by the fusion, which can be taken as the initial pose for the scan matching problem
Summary
Simultaneous localization and mapping (SLAM) provides the mobile robot the ability to set up a model of the working space and to localize itself, and it is the most important ability for a truly autonomous robot able to operate within real-world environments. Though the initial estimated pose can be optimized in the second stage (back-end stage), the accumulated errors of DA may give rise to big problems for the results of SLAM, and the accuracy and the stability will be badly affected, sometimes even failing to obtain the results This is often the case in low-texture environments for a 2D scanner, for example, the mobile robot moves along the long corridor in an indoor environment, and due to the measuring limitation of the scanner, there may be no obvious changes between two consecutive scanning outputs, making the DA process difficult. Based on EKF framework, the information from the IMU sensor is integrated with the 2D LiDAR sensor, and an initial motion estimation can be obtained by the fusion, which can be taken as the initial pose for the scan matching problem This greatly improves the accuracy and stability of the DA results under the low-texture and dynamic environment. Quantitative experiments are conducted to evaluate the proposed method
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
