Visual-Inertial RGB-D SLAM with Encoders for a Differential Wheeled Robot
arXiv:2610.41768 · doi:10.1109/JSEN.2021.3101370
Abstract
Recent years have seen multiple impressive results in visual-inertial odometry (VIO) techniques, by which accurate state estimation can be achieved via the extended Kalman filter (EKF) or nonlinear optimization. However, these approaches are rarely open source, and they tend to fail in real experiments due to the temporary lack of feature points and fast motion. Therefore, in this study, we used encoders to overcome the temporary failure of purely vision-based simultaneous localization and mapping (SLAM). Here, we propose a generative measurement model for encoders and derive an expression for the maximum a posteriori (MAP) estimate and necessary Jacobians for optimization. We use our theory to present a novel tightly coupled visual-inertial encoder RGB-Depth (RGB-D) SLAM system. Tests on our system on an in-house dataset confirmed that our modeling effort led to accurate (with a root mean squared error (RMSE) of approximately 2-7 cm) and robust state estimation in real time. The source code and our dataset containing the encoder information have been published for verification.
arXiv comment11 pages. Author-accepted manuscript of the article published in IEEE Sensors Journal, 2022
References in corpus (5)
- ORB-SLAM: a Versatile and Accurate Monocular SLAM System
- ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras
- VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator
- On-Manifold Preintegration for Real-Time Visual-Inertial Odometry
- Visual-Inertial Monocular SLAM with Map Reuse