Visual-Inertial RGB-D SLAM with Encoders for a Differential Wheeled Robot
Overview
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.
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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.”
Exact source · revision 1Source owner not reported
Artifact availability · code: reported pending
“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.”
Exact source · revision 1Source owner not reported
Artifact availability · dataset: reported pending
“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.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalVisual-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.
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