ArticleBiomimetics (Basel, Switzerland)2025
Multi-Sensor Fusion Framework for Reliable Localization and Trajectory Tracking of Mobile Robot by Integrating UWB, Odometry, and AHRS.
Article in Biomimetics (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- ACO-CLS: Ant Colony Optimization-Based Collaborative Localization and Search for Multi-Robot Systems.Sensors (Basel, Switzerland) · 2026Article
- Enhanced Indoor Mobile Robot Localization via Lie-Group IMU-UWB Fusion and Dual-Stage Kalman Filtering.Sensors (Basel, Switzerland) · 2026Article
- A Novel Shallow Neural Network-Augmented Pose Estimator Based on Magneto-Inertial Sensors for Reference-Denied Environments.Sensors (Basel, Switzerland) · 2025Article
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Authors and funding
2 authors.
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Abstract
This paper presents a multi-sensor fusion framework for the accurate indoor localization and trajectory tracking of a differential-drive mobile robot. The proposed system integrates Ultra-Wideband (UWB) trilateration, wheel odometry, and Attitude and Heading Reference System (AHRS) data using a Kalman filter. This fusion approach reduces the impact of noisy and inaccurate UWB measurements while correcting odometry drift. The system combines raw UWB distance measurements with wheel encoder readings and heading information from an AHRS to improve robustness and positioning accuracy. Experimental validation was conducted through repeated closed-loop trajectory trials. The results demonstrate that the proposed method significantly outperforms UWB-only localization, yielding reduced noise, enhanced consistency, and lower Dynamic Time Warping (DTW) distances across repetitions. The findings confirm the system's effectiveness and suitability for real-time mobile robot navigation in indoor environments.
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