ImLPR: Image-based LiDAR Place Recognition using Vision Foundation Models

Minwoo Jung, Lanke Frank Tarimo Fu, Maurice Fallon, Ayoung Kim
Proceedings of The 9th Conference on Robot Learning, PMLR 305:3318-3340, 2025.

Abstract

LiDAR Place Recognition (LPR) is a key component in robotic localization, enabling robots to align current scans with prior maps of their environment. While Visual Place Recognition (VPR) has embraced Vision Foundation Models (VFMs) to enhance descriptor robustness, LPR has relied on task-specific models with limited use of pre-trained foundation-level knowledge. This is due to the lack of 3D foundation models and the challenges of using VFM with LiDAR point clouds. To tackle this, we introduce ImLPR, a novel pipeline that employs a pre-trained DINOv2 VFM to generate rich descriptors for LPR. To our knowledge, ImLPR is the first method to leverage a VFM to support LPR. ImLPR converts raw point clouds into Range Image Views (RIV) to leverage VFM in the LiDAR domain. It employs MultiConv adapters and Patch-InfoNCE loss for effective feature learning. We validate ImLPR using public datasets where it outperforms state-of-the-art (SOTA) methods in intra-session and inter-session LPR with top Recall@1 and F1 scores across various LiDARs. We also demonstrate that RIV outperforms Bird’s-Eye-View (BEV) as a representation choice for adapting LiDAR for VFM. We release ImLPR as open source for the robotics community.

Cite this Paper

BibTeX
@InProceedings{pmlr-v305-jung25c, title = {ImLPR: Image-based LiDAR Place Recognition using Vision Foundation Models}, author = {Jung, Minwoo and Fu, Lanke Frank Tarimo and Fallon, Maurice and Kim, Ayoung}, booktitle = {Proceedings of The 9th Conference on Robot Learning}, pages = {3318--3340}, year = {2025}, editor = {Lim, Joseph and Song, Shuran and Park, Hae-Won}, volume = {305}, series = {Proceedings of Machine Learning Research}, month = {27--30 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v305/main/assets/jung25c/jung25c.pdf}, url = {https://proceedings.mlr.press/v305/jung25c.html}, abstract = {LiDAR Place Recognition (LPR) is a key component in robotic localization, enabling robots to align current scans with prior maps of their environment. While Visual Place Recognition (VPR) has embraced Vision Foundation Models (VFMs) to enhance descriptor robustness, LPR has relied on task-specific models with limited use of pre-trained foundation-level knowledge. This is due to the lack of 3D foundation models and the challenges of using VFM with LiDAR point clouds. To tackle this, we introduce ImLPR, a novel pipeline that employs a pre-trained DINOv2 VFM to generate rich descriptors for LPR. To our knowledge, ImLPR is the first method to leverage a VFM to support LPR. ImLPR converts raw point clouds into Range Image Views (RIV) to leverage VFM in the LiDAR domain. It employs MultiConv adapters and Patch-InfoNCE loss for effective feature learning. We validate ImLPR using public datasets where it outperforms state-of-the-art (SOTA) methods in intra-session and inter-session LPR with top Recall@1 and F1 scores across various LiDARs. We also demonstrate that RIV outperforms Bird’s-Eye-View (BEV) as a representation choice for adapting LiDAR for VFM. We release ImLPR as open source for the robotics community.} }
Endnote
%0 Conference Paper %T ImLPR: Image-based LiDAR Place Recognition using Vision Foundation Models %A Minwoo Jung %A Lanke Frank Tarimo Fu %A Maurice Fallon %A Ayoung Kim %B Proceedings of The 9th Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2025 %E Joseph Lim %E Shuran Song %E Hae-Won Park %F pmlr-v305-jung25c %I PMLR %P 3318--3340 %U https://proceedings.mlr.press/v305/jung25c.html %V 305 %X LiDAR Place Recognition (LPR) is a key component in robotic localization, enabling robots to align current scans with prior maps of their environment. While Visual Place Recognition (VPR) has embraced Vision Foundation Models (VFMs) to enhance descriptor robustness, LPR has relied on task-specific models with limited use of pre-trained foundation-level knowledge. This is due to the lack of 3D foundation models and the challenges of using VFM with LiDAR point clouds. To tackle this, we introduce ImLPR, a novel pipeline that employs a pre-trained DINOv2 VFM to generate rich descriptors for LPR. To our knowledge, ImLPR is the first method to leverage a VFM to support LPR. ImLPR converts raw point clouds into Range Image Views (RIV) to leverage VFM in the LiDAR domain. It employs MultiConv adapters and Patch-InfoNCE loss for effective feature learning. We validate ImLPR using public datasets where it outperforms state-of-the-art (SOTA) methods in intra-session and inter-session LPR with top Recall@1 and F1 scores across various LiDARs. We also demonstrate that RIV outperforms Bird’s-Eye-View (BEV) as a representation choice for adapting LiDAR for VFM. We release ImLPR as open source for the robotics community.
APA
Jung, M., Fu, L.F.T., Fallon, M. & Kim, A.. (2025). ImLPR: Image-based LiDAR Place Recognition using Vision Foundation Models. Proceedings of The 9th Conference on Robot Learning, in Proceedings of Machine Learning Research 305:3318-3340 Available from https://proceedings.mlr.press/v305/jung25c.html.

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