One RING to Rule Them All: Radon Sinogram for Place Recognition, Orientation and Translation Estimation

TL;DR

This paper introduces a Radon sinogram (RING) based framework for LiDAR global localization that achieves accurate place recognition and pose estimation with lower place density, robustness to orientation and translation, and a learning-free approach.

Contribution

It proposes a novel Radon sinogram representation for all localization sub-tasks, enabling certifiable robustness and a global convergent solver for orientation and translation estimation.

Findings

Achieves high localization accuracy with sparse place data.

Demonstrates robustness against arbitrary orientations and large translations.

Outperforms existing methods in lower place density scenarios.

Abstract

LiDAR-based global localization is a fundamental problem for mobile robots. It consists of two stages, place recognition and pose estimation, which yields the current orientation and translation, using only the current scan as query and a database of map scans. Inspired by the definition of a recognized place, we consider that a good global localization solution should keep the pose estimation accuracy with a lower place density. Following this idea, we propose a novel framework towards sparse place-based global localization, which utilizes a unified and learning-free representation, Radon sinogram (RING), for all sub-tasks. Based on the theoretical derivation, a translation invariant descriptor and an orientation invariant metric are proposed for place recognition, achieving certifiable robustness against arbitrary orientation and large translation between query and map scan. In addition, we also utilize the property of RING to propose a global convergent solver for both orientation and translation estimation, arriving at global localization. Evaluation of the proposed RING based framework validates the feasibility and demonstrates a superior performance even under a lower place density.