All-UWB SLAM Using UWB Radar and UWB AOA
Gihan Charith Premachandra Hanchapola Appuhamilage, Achala Athukorala, U-Xuan Tan
AI summary
Problem
Existing UWB radar SLAM fails in feature-deficient, vision-denied environments due to a lack of distinguishable landmarks, while standalone UWB AOA systems struggle with multi-path noise and lack effective SLAM integration.
Approach
The system dynamically deploys UWB AOA tags as artificial landmarks in featureless areas and fuses them with UWB radar observations, using a moving-window algorithm to extract prominent features and filter anomalous AOA readings.
Key results
- Proposed All-UWB SLAM framework fusing radar and dynamic AOA tags
- Developed a moving-window feature extraction method to isolate prominent points and filter multi-path noise
- Implemented a real-time ROS2 system with validated experimental results
- Demonstrated successful SLAM in unstructured, feature-deficient indoor environments
Why it matters
Enables reliable autonomous navigation for robots operating in harsh, vision-denied conditions like smoke or dust, critical for search-and-rescue and industrial inspection.
Abstract
There has been a growing interest in autonomous systems designed to operate in adverse conditions (e.g. smoke, dust), where the visible light spectrum fails. In this context, Ultra-wideband (UWB) radar is capable of penetrating through such challenging environmental conditions due to the lower frequency components within its broad bandwidth. Therefore, UWB radar has emerged as a potential sensing technology for Simultaneous Localization and Mapping (SLAM) in vision-denied environments where optical sensors (e.g. LiDAR, Camera) are prone to failure. Existing approaches involving UWB radar as the primary exteroceptive sensor generally extract features in the environment, which are later initialized as landmarks in a map. However, these methods are constrained by the number of distinguishable features in the environment. Hence, this paper proposes a novel method incorporating UWB Angle of Arrival (AOA) measurements into UWB radar-based SLAM systems to improve the accuracy and scalability of SLAM in feature- deficient environments. The AOA measurements are obtained using UWB anchor-tag units which are dynamically deployed by the robot in featureless areas during mapping of the environment. This paper thoroughly discusses prevailing constraints associated with UWB AOA measurement units and presents solutions to overcome them. Our experimental results show that integrating UWB AOA units with UWB radar enables SLAM in vision-denied feature-deficient environments.