# Automatic Coverage Selection for Surface-Based Visual Localization

## TL;DR

This paper introduces methods to automatically optimize sensor coverage in visual localization systems, balancing environmental perception and computational efficiency for autonomous robots and vehicles.

## Contribution

It presents the first approach to automatically determine minimal sensor coverage for optimal localization performance using a novel performance indicator.

## Key findings

- The localization performance indicator effectively predicts localization success.
- The method successfully optimizes coverage with minimal calibration data.
- Demonstrated on real-world aerial and ground datasets.

## Abstract

Localization is a critical capability for robots, drones and autonomous vehicles operating in a wide range of environments. One of the critical considerations for designing, training or calibrating visual localization systems is the coverage of the visual sensors equipped on the platforms. In an aerial context for example, the altitude of the platform and camera field of view plays a critical role in how much of the environment a downward facing camera can perceive at any one time. Furthermore, in other applications, such as on roads or in indoor environments, additional factors such as camera resolution and sensor placement altitude can also affect this coverage. The sensor coverage and the subsequent processing of its data also has significant computational implications. In this paper we present for the first time a set of methods for automatically determining the trade-off between coverage and visual localization performance, enabling the identification of the minimum visual sensor coverage required to obtain optimal localization performance with minimal compute. We develop a localization performance indicator based on the overlapping coefficient, and demonstrate its predictive power for localization performance with a certain sensor coverage. We evaluate our method on several challenging real-world datasets from aerial and ground-based domains, and demonstrate that our method is able to automatically optimize for coverage using a small amount of calibration data. We hope these results will assist in the design of localization systems for future autonomous robot, vehicle and flying systems.