Ultra-Wideband Aided Fast Localization and Mapping System
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Abstract (arXiv)
This paper proposes an ultra-wideband (UWB) aided localization and mapping system that leverages on inertial sensor and depth camera. Inspired by the fact that visual odometry (VO) system, regardless of its accuracy in the short term, still faces challenges with accumulated errors in the long run or under unfavourable environments, the UWB ranging measurements are fused to remove the visual drift and improve the robustness. A general framework is developed which consists of three parallel threads, two of which carry out the visual-inertial odometry (VIO) and UWB localization respectively. The other mapping thread integrates visual tracking constraints into a pose graph with the proposed smooth and virtual range constraints, such that an optimization is performed to provide robust trajectory estimation. Experiments show that the proposed system is able to create dense drift-free maps in real-time even running on an ultra-low power processor in featureless environments.
Key facts. Cited by 3 papers in the map (data through 13 Oct 2026). First posted on arXiv 30 Sep 2017.
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Citing papers per year: 2024: 3.
3 papers.
- 2024MCD: Diverse Large-Scale Multi-Campus Dataset for Robot Perceptioncited by 15
- 2024ULOC: Learning to Localize in Complex Large-Scale Environments with Ultra-Wideband Rangescited by 2
- 2024Multi-Robot Relative Pose Estimation in SE(2) with Observability Analysis: A Comparison of Extended Kalman Filtering and Robust Pose Graph Optimizationcited by 0
- 2024ULOC: Learning to Localize in Complex Large-Scale Environments with Ultra-Wideband Ranges17 Sep 2024
- 2024MCD: Diverse Large-Scale Multi-Campus Dataset for Robot Perception18 Mar 2024
- 2024Multi-Robot Relative Pose Estimation in SE(2) with Observability Analysis: A Comparison of Extended Kalman Filtering and Robust Pose Graph Optimization27 Jan 2024