Multi-sensor fusion plays a key role in 2D laser-based robot location and navigation. Although it has achieved great success, there are still some challenges, e.g., being fragile when having a large angular rotation. In this paper, we present a deep learning-based approach to localizing a mobile robot using a 2D laser and an inertial measurement unit. A novel recurrent convolutional neural network (RCNN)-based architecture is developed to fuse laser and inertial data for scan-to-scan pose estimation. A scan-to-submap optimization is also introduced to optimize the poses estimated by the RCNN for enhanced robustness and accuracy. Extensive experiments have been conducted in both simulation and practice with a real mobile robot, verifying the effectiveness of the proposed deep sensor fusion system.
- 2D laser scanning
- Data fusion
- inertial measurement unit (IMU)
- pose estimation
ASJC Scopus subject areas
- Electrical and Electronic Engineering