All-UWB SLAM Using UWB Radar and UWB AOA

2025-07-21

AI Summary

Visual SLAM suffers from accuracy and scalability limitations in visually degraded, texture-poor environments. Method: This paper proposes a tightly coupled SLAM framework integrating ultra-wideband (UWB) radar with UWB angle-of-arrival (AOA) measurements. It is the first to incorporate AOA information into the UWB radar SLAM pipeline and enables autonomous deployment of dynamic UWB anchors by the robot, eliminating reliance on pre-installed infrastructure in featureless scenes. Pose estimation and sparse mapping are achieved via joint modeling and optimization of radar echoes and AOA measurements. Results: The method operates robustly without any visual input—under severe occlusion (e.g., smoke/dust) and in low-texture settings—achieving a 37% improvement in localization accuracy. It significantly enhances practicality and scalability for demanding industrial and emergency-response applications.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Enhances SLAM accuracy in feature-deficient environments using UWB AOA.
• Overcomes limitations of UWB radar in feature-sparse conditions.
• Integrates UWB radar and AOA for vision-denied environment navigation.

Innovation

• Uses UWB radar for vision-denied SLAM
• Integrates UWB AOA for feature-deficient areas
• Dynamically deploys UWB anchor-tag units