Calibrating Panoramic Depth Estimation for Practical Localization and Mapping

TL;DR

This paper introduces a self-supervised calibration method for panoramic depth estimation that improves accuracy and robustness, enabling better localization and mapping in practical robotics applications.

Contribution

It proposes a novel self-supervised calibration technique that refines panoramic depth estimates using geometric consistency without additional data collection.

Findings

Significant performance improvements in robot navigation tasks.

Enhanced accuracy of depth estimation under domain shifts.

Robustness of localization in diverse external conditions.

Abstract

The absolute depth values of surrounding environments provide crucial cues for various assistive technologies, such as localization, navigation, and 3D structure estimation. We propose that accurate depth estimated from panoramic images can serve as a powerful and light-weight input for a wide range of downstream tasks requiring 3D information. While panoramic images can easily capture the surrounding context from commodity devices, the estimated depth shares the limitations of conventional image-based depth estimation; the performance deteriorates under large domain shifts and the absolute values are still ambiguous to infer from 2D observations. By taking advantage of the holistic view, we mitigate such effects in a self-supervised way and fine-tune the network with geometric consistency during the test phase. Specifically, we construct a 3D point cloud from the current depth prediction and project the point cloud at various viewpoints or apply stretches on the current input image to generate synthetic panoramas. Then we minimize the discrepancy of the 3D structure estimated from synthetic images without collecting additional data. We empirically evaluate our method in robot navigation and map-free localization where our method shows large performance enhancements. Our calibration method can therefore widen the applicability under various external conditions, serving as a key component for practical panorama-based machine vision systems. Code is available through the following link: \url{https://github.com/82magnolia/panoramic-depth-calibration}.