Learning-Based Distance Estimation for 360° Single-Sensor Setups
Journal:
arXiv
Published Date:
Jun 25, 2025
Abstract
Accurate distance estimation is a fundamental challenge in robotic
perception, particularly in omnidirectional imaging, where traditional
geometric methods struggle with lens distortions and environmental variability.
In this work, we propose a neural network-based approach for monocular distance
estimation using a single 360{\deg} fisheye lens camera. Unlike classical
trigonometric techniques that rely on precise lens calibration, our method
directly learns and infers the distance of objects from raw omnidirectional
inputs, offering greater robustness and adaptability across diverse conditions.
We evaluate our approach on three 360{\deg} datasets (LOAF, ULM360, and a newly
captured dataset Boat360), each representing distinct environmental and sensor
setups. Our experimental results demonstrate that the proposed learning-based
model outperforms traditional geometry-based methods and other learning
baselines in both accuracy and robustness. These findings highlight the
potential of deep learning for real-time omnidirectional distance estimation,
making our approach particularly well-suited for low-cost applications in
robotics, autonomous navigation, and surveillance.
