Improving Low-Cost Solutions for Path Mapping in Autonomous Vehicle

Abstract

One of the main challenge in designing autonomous vehicle is developing the algorithms necessary for simultaneous localization and mapping (SLAM). While this process heavily depends on an expensive hardware called Light Detection and Ranging (LiDAR), there are cheaper alternatives that can be implemented in earlier stage of autonomous vehicle development. Inertial Measurement Unit (IMU), vehicle odometry, and Global Positioning System (GPS) can be used as a solution. The main problem is these cheaper alternatives relies heavily on the precision of the hardware used. In this project, we outline the mapping process using these cheaper solutions which can also be used to complement LiDAR-based SLAM. We show that using simple approaches such as static bias drift removal, high-pass filtering, signal downsampling and linear interpolation, we can increase the robustness and improve the accuracy and precision of the IMU and GPS used respectively. We manage to increase the precision of the GPS readings and reduce the drift of IMU on average from -17.105 deg/min to -0.1177 deg/min. We show the improvement achieved by our proposed method by mapping the road around Engine Square, Jalan Ilmu 1/1, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia.