Problem
Research questions and friction points this paper is trying to address.
radar-lidar localization
sensing-modality gap
SE(3)
Dr-LiSA: Direct Radar-Lidar Scan Alignment for $SE(3)$ Localization
🤖 AI Summary
该论文提出Dr-LiSA，通过学习的前向模型预测雷达测量值，并直接对齐预测和实际雷达扫描以解决雷达-激光雷达在SE(3)中的定位问题。
📝 Abstract
This paper introduces Dr-LiSA, a first-of-its-kind direct method for localizing 2D spinning radar intensity measurements in $SE(3)$ against 3D lidar maps. Radar-lidar localization combines the complementary strengths of the two sensing modalities: radar is robust to adverse weather and precipitation, while lidar provides high-fidelity 3D maps in favourable conditions. However, existing radar-lidar localization methods are restricted to planar $SE(2)$ localization and have generally fallen short of the accuracy achieved by lidar-lidar and even radar-radar systems. A key challenge is the substantial sensing-modality gap between radar and lidar, which observe and represent scene structure in fundamentally different ways. Dr-LiSA bridges this gap using a learned forward model that predicts radar measurements from a lidar submap at a candidate pose, enabling direct photometric alignment of predicted and observed radar scans in $SE(3)$. Dr-LiSA outperforms prior radar-lidar approaches in $SE(2)$ while achieving planar accuracy competitive with state-of-the-art radar-radar localization across more than 90 km of on-road data.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Direct Radar-Lidar Scan Alignment
Learned Forward Model
SE(3) Localization
Photometric Alignment
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