Visual-Inertial RGB-D SLAM with Encoders for a Differential Wheeled Robot
Visual-Inertial RGB-D SLAM with Encoders for a Differential Wheeled Robot
This study introduces a novel tightly coupled visual-inertial encoder RGB-Depth (RGB-D) SLAM system designed for differential wheeled robots. By incorporating a generative measurement model for encoders and deriving expressions for maximum a posteriori (MAP) estimates, the approach aims to overcome limitations of purely vision-based simultaneous localization and mapping (SLAM), such as temporary feature point scarcity and fast motion. Tests on an in-house dataset demonstrate accurate and robust real-time state estimation with a root mean squared error (RMSE) of approximately 2-7 cm. The source code and dataset containing encoder information are published for verification.
The authors propose a tightly coupled visual-inertial encoder RGB-D SLAM system that integrates encoder measurements to address feature scarcity and fast motion limitations in purely vision-based approaches, reporting an RMSE of approximately 2-7 cm on an in-house dataset with source code and data released for verification.
Source: arXiv Robotics — research abstracts · Read original article ↗
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RMSE
2–7
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ests 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.
Open source S4
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- code: Reported pending Open source S4
- dataset: Reported pending Open source S4
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ests 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.
Open source S4
Visual-Inertial RGB-D SLAM with Encoders for a Differential Wheeled Robot
Open source S1
arXiv:2610.41768v1 Announce Type: new 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. Therefo
Open source S2
re, 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. T
Open source S3
Source：arXiv Robotics — research abstracts · arxiv.org