Integration of UWB Radar on Mobile Robots for Continuous Obstacle and Environment Mapping

TL;DR

This work tackles obstacle detection and environmental mapping for mobile robots in visibility-limited environments by mounting UWB radar and operating without fixed anchors. It introduces a three-step pipeline—target identification from CIR peaks, robust filtering using peak properties, SNR, and PDoA, and clustering-based mapping using distance and AoA estimates—to produce accurate obstacle localization. Experimental results on channels 5 and 9 across metal, concrete, and plywood demonstrate high precision (above 90% precision) and strong distance accuracy, particularly on channel 9, with a median mapping error around 8.5 cm and sub-25 cm errors for the majority of cases. The approach offers a foundation for anchor-free UWB SLAM and low-weight, low-power radar-enabled navigation without relying on visual features or fixed infrastructure.

Abstract

This paper presents an infrastructure-free approach for obstacle detection and environmental mapping using ultra-wideband (UWB) radar mounted on a mobile robotic platform. Traditional sensing modalities such as visual cameras and Light Detection and Ranging (LiDAR) fail in environments with poor visibility due to darkness, smoke, or reflective surfaces. In these visioned-impaired conditions, UWB radar offers a promising alternative. To this end, this work explores the suitability of robot-mounted UWB radar for environmental mapping in dynamic, anchor-free scenarios. The study investigates how different materials (metal, concrete and plywood) and UWB radio channels (5 and 9) influence the Channel Impulse Response (CIR). Furthermore, a processing pipeline is proposed to achieve reliable mapping of detected obstacles, consisting of 3 steps: (i) target identification (based on CIR peak detection), (ii) filtering (based on peak properties, signal-to-noise score, and phase-difference of arrival), and (iii) clustering (based on distance estimation and angle-of-arrival estimation). The proposed approach successfully reduces noise and multipath effects, resulting in an obstacle detection precision of at least 90.71% and a recall of 88.40% on channel 9 even when detecting low-reflective materials such as concrete. This work offers a foundation for further development of UWB-based localisation and mapping (SLAM) systems that do not rely on visual features and, unlike conventional UWB localisation systems, do not require on fixed anchor nodes for triangulation.

Figures (16)

• Figure 1: Example of a Channel Impulse Response where the object is placed at a distance of 3.5 m.
• Figure 2: Overview of the proposed approach: Each capture contains the preamble CIR and two additional CIRs from both antennas on the same chip used for calculating the PDoA. A new measurement happens every 10.42 ms and contains the data of the environment at one timestamp. These are processed independently followed by filtering out noise or unwanted multipath components using peak properties, SNR-score, and PDoA. The distance to the object is estimated and the AoA is then calculated. Subsequently, multiple rows are combined to cluster reflections from the same object and map the environment.
• Figure 3: The CIRs measured for metal, concrete and plywood objects, overlaid on a background CIR measured without any obstacles. The reflections caused by each material appear as distinct peaks at specific delays relative to the first path. The metal object (blue) is clearly visible compared to the background.
• Figure 4: Definition of prominence, width, and the SNR-based score. Prominence is the vertical distance between a peak and its lowest contour line. In this example, the prominence of the potential object's peak is measured relative to contour line CL1 rather than CL2, since CL2 has a lower value. The width is calculate at half the prominence, using interpolation to determine the intersection points. The SNR-score evaluates the detected peak relative to the noise floor.
• Figure 5: