用视觉模型提升激光雷达定位，让机器人更准认路。
ImLPR: Image-based LiDAR Place Recognition using Vision Foundation Models
- 将点云转为三通道图像，借视觉大模型提取特征。
- 在多个数据集上超越现有方法，跨会话识别准确率提升显著。
- 开源代码，适合做机器人定位的开发者使用。
激光雷达位置识别（LPR）是机器人定位的关键，帮助机器人将当前扫描与环境地图对齐。尽管视觉位置识别（VPR）已广泛采用视觉基础模型（VFMs）增强特征鲁棒性，但LPR仍依赖专用模型，未能充分利用预训练的基础知识，这主要受限于缺乏3D基础模型以及将视觉模型用于点云的挑战。为此，我们提出ImLPR，首个利用预训练DINOv2 VFM进行LPR的方法，保留大部分预训练知识。ImLPR将原始点云转换为三通道范围图像视图（RIV），以在激光雷达领域发挥视觉模型优势，并采用多卷积适配器和补丁级InfoNCE损失进行有效特征学习。我们在公开数据集上验证了ImLPR，在多种评估指标下均优于当前最优（SOTA）方法，涵盖同会话与跨会话场景。通过全面消融实验，量化了通道组合、RIV、适配器及补丁级损失等关键设计的影响。我们已将ImLPR开源：https://github.com/minwoo0611/ImLPR。
原文摘要 · Abstract (English)
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 the best of our knowledge, ImLPR is the first method to utilize a VFM for LPR while retaining the majority of pre-trained knowledge. ImLPR converts raw point clouds into novel three-channel 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 on public datasets and outperform state-of-the-art (SOTA) methods across multiple evaluation metrics in both intra- and inter-session LPR. Comprehensive ablations on key design choices such as channel composition, RIV, adapters, and the patch-level loss quantify each component's impact. We release ImLPR as open source for the robotics community: https://github.com/minwoo0611/ImLPR.
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