Learning Where to Look: Self-supervised Viewpoint Selection for Active Localization using Geometrical Information

TL;DR

This paper introduces a self-supervised, data-driven approach for active viewpoint selection to improve localization accuracy in robotics, demonstrating superior performance over existing methods on synthetic and real data.

Contribution

It proposes a simple, real-time capable architecture for viewpoint selection, utilizing self-supervised training and integrating with planning frameworks for enhanced localization.

Findings

Outperforms existing methods in localization accuracy

Works effectively on both synthetic and real datasets

Supports real-time operation in robotics applications

Abstract

Accurate localization in diverse environments is a fundamental challenge in computer vision and robotics. The task involves determining a sensor's precise position and orientation, typically a camera, within a given space. Traditional localization methods often rely on passive sensing, which may struggle in scenarios with limited features or dynamic environments. In response, this paper explores the domain of active localization, emphasizing the importance of viewpoint selection to enhance localization accuracy. Our contributions involve using a data-driven approach with a simple architecture designed for real-time operation, a self-supervised data training method, and the capability to consistently integrate our map into a planning framework tailored for real-world robotics applications. Our results demonstrate that our method performs better than the existing one, targeting similar problems and generalizing on synthetic and real data. We also release an open-source implementation to benefit the community.