Camera-RFID Fusion for Robust Asset Tracking in Forested Environments

2026-04-28

AI Summary

This work addresses the challenge of robust asset tracking in forest environments, where RFID-based localization suffers from signal attenuation and multipath effects, while purely vision-based methods are prone to occlusion and spatial ambiguity. To overcome these limitations, the study presents the first integration of passive RFID and camera systems in natural forest settings, leveraging stereo vision, depth sensing, object detection, and trajectory matching algorithms for multimodal cooperative localization. The proposed approach effectively bridges the accuracy gap between meter-level RFID and centimeter-level visual localization, enabling high-precision and robust tracking even when assets are temporarily occluded or located beyond the line of sight. This significantly enhances localization reliability in densely cluttered and obstructed forest environments.

Abstract

Passive RFID tags offer a cost-effective and scalable solution for tracking numerous deployed assets. However, in forested environments, signal attenuation and multipath effects generally limit RFID spatial accuracy to the meter level. Conversely, while cameras employing stereo vision can achieve centimeter-level precision, relying solely on computer vision fails to resolve issues arising from spatial association ambiguity and partial occlusions in dense settings. Fusing these modalities allows systems to harness the high-accuracy benefits of vision while retaining the robust, non-line-of-sight identification advantages of RFID. Yet, a primary challenge in achieving this, which is the central focus of this paper, lies in accurately associating the disparate trajectories generated by these two sensors. To overcome this limitation, we introduce a novel camera--RFID fusion framework that integrates depth and object information with advanced trajectory-matching algorithms. By successfully bridging the meter-to-centimeter accuracy gap, the proposed approach helps achieve reliable tag localization even when assets temporarily leave the camera's field of view. To the best of our knowledge, this represents the first application of camera--RFID fusion for asset tracking in natural forested environments.

Problem

• Camera-RFID fusion
• asset tracking
• forested environments
• trajectory association
• sensor fusion

Innovation

• camera-RFID fusion
• trajectory matching
• asset tracking
• forested environments
• multimodal sensing