Polar Perspectives: Evaluating 2-D LiDAR Projections for Robust Place Recognition with Visual Foundation Models

TL;DR

This paper systematically evaluates how different 2-D LiDAR projections impact the performance of visual foundation models in place recognition, highlighting the importance of projection design for robustness and real-time deployment.

Contribution

It introduces a modular pipeline to isolate projection effects and demonstrates that optimized 2-D projections can effectively replace complex 3-D learning for LiDAR-based place recognition.

Findings

Certain projections improve robustness to environmental changes

Optimized projections match end-to-end 3-D learning performance

Projections enable real-time autonomous navigation

Abstract

This work presents a systematic investigation into how alternative LiDAR-to-image projections affect metric place recognition when coupled with a state-of-the-art vision foundation model. We introduce a modular retrieval pipeline that controls for backbone, aggregation, and evaluation protocol, thereby isolating the influence of the 2-D projection itself. Using consistent geometric and structural channels across multiple datasets and deployment scenarios, we identify the projection characteristics that most strongly determine discriminative power, robustness to environmental variation, and suitability for real-time autonomy. Experiments with different datasets, including integration into an operational place recognition policy, validate the practical relevance of these findings and demonstrate that carefully designed projections can serve as an effective surrogate for end-to-end 3-D learning in LiDAR place recognition.