Measurement-Calibrated Multi-Camera Fusion for Vision-Based Indoor Localization

2026-06-11

AI Summary

Indoor visual localization is hindered by detection noise, occlusions, and limited camera coverage, leading to multi-stage uncertainties that existing fusion methods fail to explicitly model. This work proposes a component-level error quantification and calibration mechanism that explicitly characterizes the uncertainty in homography calibration, human detection, and motion tracking, and leverages these estimates to optimize multi-camera fusion weights. By transforming the fusion process from a black-box into an interpretable framework, the method significantly enhances trajectory stability and motion smoothness. Experimental results demonstrate that, while yielding only marginal gains in absolute localization accuracy over single-camera baselines, the proposed strategy effectively reduces trajectory variance and substantially improves the continuity and robustness of motion estimation.

Abstract

Indoor vision-based localization systems are affected by detection noise, occlusions, and limited camera coverage, leading to uncertainty at multiple stages of the pipeline. While multi-camera data fusion is widely used to mitigate these issues, it is typically treated as a black-box component and evaluated solely end-to-end, obscuring its mechanistic contributions. To address this gap, this work investigates whether explicitly characterizing single-camera localization errors can be leveraged to calibrate and optimize multi-camera data fusion. We introduce a measurement-calibrated fusion approach that integrates component-wise error quantification, specifically isolating homography calibration, human detection, and motion tracking. A component-wise evaluation is conducted to quantify error contributions from homography calibration, human detection, and motion tracking. Experimental results show that data fusion improves localization accuracy compared to single-camera baselines. While measurement-calibrated fusion provides only limited improvement in absolute accuracy over standard fusion, it substantially reduces trajectory variance and improves motion smoothness, which are critical for applications requiring stable and continuous motion estimates. These results highlight the value of explicit error characterization when designing data fusion strategies for vision-based indoor positioning systems.

Problem

• indoor localization
• multi-camera fusion
• detection noise
• occlusions
• error characterization

Innovation

• measurement-calibrated fusion
• component-wise error quantification
• multi-camera fusion
• vision-based indoor localization
• trajectory smoothness