PRISM-Loc: a Lightweight Long-range LiDAR Localization in Urban Environments with Topological Maps
Kirill Muravyev, Artem Kobozev, Vasily Yuryev, Alexander Melekhin, Oleg Bulichev, Dmitry Yudin, Konstantin Yakovlev

TL;DR
PRISM-Loc is a lightweight, real-time LiDAR localization method for urban environments that uses topological maps and novel scan-matching techniques, achieving high accuracy on large-scale datasets with minimal resource usage.
Contribution
It introduces a compact topological representation combined with a novel curb detection and scan matching pipeline for resource-constrained urban localization.
Findings
99% success rate on ITLP-Campus dataset
150 ms per localization on embedded platform
20 MB map size for city-scale localization
Abstract
We propose PRISM-Loc - a lightweight and robust approach for localization in large outdoor environments that combines a compact topological representation with a novel scan-matching and curb-detection module operating on raw LiDAR scans. The method is designed for resource-constrained platforms and emphasizes real-time performance and resilience to common urban sensing challenges. It provides accurate localization in compact topological maps using global place recognition and an original scan matching technique. Experiments on standard benchmarks and on an embedded platform demonstrate the effectiveness of our approach. Our method achieves a 99\% success rate on the large-scale ITLP-Campus dataset while running at 150 ms per localization and using a 20 MB map for localization. We highlight three main contributions: (1) a compact representation for city-scale localization; (2) a novel…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Indoor and Outdoor Localization Technologies · Advanced Neural Network Applications
