PRISM-Loc: a Lightweight Long-range LiDAR Localization in Urban Environments with Topological Maps
This work addresses the challenge of real-time, memory-efficient localization for mobile robots or self-driving vehicles in large urban environments, representing an incremental improvement over existing methods.
The authors tackled the problem of long-range LiDAR localization in urban environments by proposing PRISM-Loc, a lightweight method using topological maps, which outperformed state-of-the-art competitors in quality and computational efficiency on a 3 km route in the ITLP-Campus dataset.
Localization in the environment is one of the crucial tasks of navigation of a mobile robot or a self-driving vehicle. For long-range routes, performing localization within a dense global lidar map in real time may be difficult, and the creation of such a map may require much memory. To this end, leveraging topological maps may be useful. In this work, we propose PRISM-Loc -- a topological map-based approach for localization in large environments. The proposed approach leverages a twofold localization pipeline, which consists of global place recognition and estimation of the local pose inside the found location. For local pose estimation, we introduce an original lidar scan matching algorithm, which is based on 2D features and point-based optimization. We evaluate the proposed method on the ITLP-Campus dataset on a 3 km route, and compare it against the state-of-the-art metric map-based and place recognition-based competitors. The results of the experiments show that the proposed method outperforms its competitors both quality-wise and computationally-wise.