Evidential SLAM Fusing 2D Laser Scanner and Stereo Camera

Abstract

This work introduces a new complete Simultaneous Localization and Mapping (SLAM) framework that uses an enriched representation of the world based on sensor fusion and is able to simultaneously provide an accurate localization of the vehicle. A method to create an Evidential grid representation from two very different sensors, laser scanner and stereo camera, allows a better handling of the dynamic aspects of the urban environment and a proper management of errors to create a more reliable map, thus having a more precise localization. A life-long layer with high level states is presented, it maintains a global map of the entire vehicle’s trajectory and distinguishes between static and dynamic obstacles. Finally, we propose a method that at each current map creation estimates the vehicle’s position by a grid matching algorithm based on image registration techniques. Results on a real road dataset show that the environment mapping data can be improved by adding relevant information that could be missed without the proposed approach. Moreover, the proposed localization method is able to reduce the drift and improve the localization compared to other methods using similar configurations.

Robust Tightly Coupled Pose Measurement Based on Multi-Sensor Fusion in Mobile Robot System

“…Accordingly, sensors with scale measurement capabilities and monocular vision sensors are used to perform fusion vision SLAM to increase accuracy and robustness. Relatively stable and reliable solutions can be obtained with lasers [ 5 ]; however, at present, this method is generally only suitable for large-scale scenarios, such as unmanned driving, and is unsuitable for applications with limited costs. The inertial measurement unit (IMU) has become a generally accepted option.…”