Reflection-Aware Reasoning for Non-Line-of-Sight Pedestrian Localization

Byeonggyu Park; Mingu Jeon; Seong-Woo Kim

cs.ROSep 23, 2026

Reflection-Aware Reasoning for Non-Line-of-Sight Pedestrian Localization

Authors: Byeonggyu Park, Mingu Jeon, Seong-Woo Kim

Organizations: Seoul National University, Seoul, Republic of Korea · Pusan National University, Busan, Republic of Korea

Abstract

Reliable localization of non-line-of-sight (NLOS) pedestrians is critical for safe urban autonomous driving, yet it remains highly challenging in ego-dynamic outdoor environments, where ego-vehicle motion makes radar multipath propagation complex and noisy. In this paper, we present a reflection-aware framework for NLOS pedestrian localization with a moving ego-vehicle in outdoor testbed scenarios. Our framework fuses front-view camera images and 2D radar point clouds to infer reflection orders and reflective surface distributions in bird's-eye-view space. It then uses physics-guided ray tracing to reconstruct distorted reflection paths and localize the hidden pedestrian. We validate the framework in outdoor testbed scenarios under ego-dynamic conditions. The results demonstrate the effectiveness of the proposed framework for NLOS pedestrian localization with a moving ego-vehicle.

Wireless Localization

Wireless localization uses radio signals and sensor fusion to estimate the positions and trajectories of people, devices, and transmitters.

See all 65 papers

CAT-Free: Multi-View Pedestrian Localization without Calibration, Annotations, or Target-Scene Training via Adaptive Geometric Filtering
Sep 28, 2026Taigo Sakai, Hiroki Kouno, Naoki Kato +1Multi-View Geometry Camera Pose Estimation
Meijo University Department of Science Technology · Chubu Electric Power Co., Inc.
CFEAR-Teach-and-Repeat: Fast and Accurate Radar-only Localization
Mar 6, 2026Maximilian Hilger, Daniel Adolfsson, Ralf Becker +2Robot Localization Map-Based Localization
Chair of Perception for Intelligent Systems, Munich Institute of Robotics and Machine Intelligence, Technical University of Munich, Munich, Germany · Robot Navigation and Perception Lab of the AASS Research Center, Örebro University, Örebro, Sweden · Bosch Rexroth, Germany
Image Frame Dynamic Object Segmentation and Ego Motion Estimation using Radar Image Fusion
Sep 19, 2026Astik Srivastava, Suhani Grover, Avinash Sharma +1Autonomous Driving Perception Video Object Segmentation

Sep 28, 2026J/K move · Enter open · S save

CAT-Free: Multi-View Pedestrian Localization without Calibration, Annotations, or Target-Scene Training via Adaptive Geometric Filtering

Taigo Sakai, Hiroki Kouno, Naoki Kato, Kazuhiro Hotta

Meijo University Department of Science Technology · Chubu Electric Power Co., Inc.

Multi-View Geometry Camera Pose Estimation

Open paper AskPDF arXiv

Multi-camera pedestrian localization is useful for wide-area monitoring in public and commercial spaces. However, deploying these systems often requires considerable setup for each new environment. Existing methods typically require camera calibration, position annotations, or target-scene training. CAT-Free removes all three requirements. It uses synchronized RGB video as its only scene-specific input. Camera configuration is estimated directly from the video. Pedestrian locations are then estimated by combining observations from multiple cameras. Automatic camera estimation is not always accurate. This can produce unreliable pedestrian locations. CAT-Free therefore introduces two adaptive geometric filters. They remove unreliable position estimates. Their thresholds are estimated from each input sequence. CAT-Free achieves 82.5, 84.5, and 65.7 MODA on WildTrack, MultiviewX, and GMVD. It uses no supplied calibration, position annotations, or target-scene training. Published methods using such scene-specific information report 88.2--95.0 MODA on WildTrack and 83.9--96.5 on MultiviewX under their respective protocols. CAT-Free also transfers without retuning. It reaches 74.9 MODA on four additional sequences and 78.6 on an unseen 8-camera installation. Finally, localization uncertainty predicts MODA with

r=-0.98

. This provides a label-free estimate of localization reliability.

Abstract

Wireless Localization

Explore similar work

CAT-Free: Multi-View Pedestrian Localization without Calibration, Annotations, or Target-Scene Training via Adaptive Geometric Filtering

CFEAR-Teach-and-Repeat: Fast and Accurate Radar-only Localization

Image Frame Dynamic Object Segmentation and Ego Motion Estimation using Radar Image Fusion