Scene coordinate regression (SCR) achieves strong performance in outdoor LiDAR localization, but it usually requires scene-specific training that can take days, limiting practical deployment. Recent w...
LightLoc++ introduces a sensor‑robust framework for outdoor LiDAR localization that decouples a pretrained backbone from lightweight scene‑specific heads, enabling efficient new‑scene training. The authors present SULID, a synchronized urban multi‑LiDAR dataset featuring 32‑, 64‑, and 128‑beam sensors, which is used to pretrain a backbone via cross‑sensor consistency learning. Additional techniques such as sample classification guidance and redundant sample downsampling reduce regression ambiguity and computational load, achieving state‑of‑the‑art performance with minimal new‑scene training cost.
arXiv:2609.14469v1 Announce Type: new Abstract: Accurate 3D localization of unmanned aerial vehicles (UAVs) remains challenging for existing multimodal approaches due to sparse LiDAR geometry, modali...
arXiv:2609.23541v1 Announce Type: new Abstract: Multimodal 3D object detection is fundamental to robust perception in autonomous driving because it integrates complementary information from LiDAR and...
The paper introduces TempLoc, a Temporal‑aware Localization framework that improves outdoor LiDAR relocalization by leveraging spatio‑temporal consistency across scans. It first predicts point‑wise global coordinates with uncertainties, then estimates inter‑frame correspondences using an attention‑based Prior Coordinate Generation module, and finally fuses these predictions in an uncertainty‑guided manner to produce a more accurate global 6‑DoF pose. Experiments on the NCLT and Oxford RobotCar datasets show that TempLoc significantly outperforms existing state‑of‑the‑art methods.
3D object detection is the backbone of perception for automated vehicles (AV) and broader intelligent transportation systems applications. Long-range detection is challenging because sensing evidence is sparse; yet this ``long-range'' scenario is routine in traffic.
arXiv:2307. 06647v4 Announce Type: replace-cross Abstract: We propose DeepIPCv2, an end-to-end autonomous driving framework that integrates LiDAR-based environmental perception with command-specific control learning.
arXiv:2608. 19522v1 Announce Type: cross Abstract: Scan-to-map LiDAR odometry drifts unboundedly along the unobservable axes of geometrically degenerate environments like tunnels and corridors, and existing degeneracy handling requires environment-specific parameter tuning.
arXiv:2606. 09634v1 Announce Type: cross Abstract: 3D object detection is the backbone of perception for automated vehicles (AV) and broader intelligent transportation systems applications.
DXPR is a depth‑based cross‑modal place recognition framework that matches monocular camera queries to a LiDAR map using a single vision foundation model backbone. By converting both modalities into a unified depth image representation, DXPR learns modality‑invariant global descriptors without modality‑specific encoders. A geometry‑aware overlap miner refines pairwise metric learning by computing pixel‑level overlap scores, and extensive tests on KITTI and Boreas show strong performance across seasons, weather, and day/night conditions, outperforming prior CMPR baselines.
Cross-modal place recognition (CMPR) aims to identify the same location across heterogeneous sensing modalities, such as vision and LiDAR. Existing methods commonly bridge the modality gap using complex alignment modules, multi-stage training, or full fine-tuning of pretrained backbones.
arXiv:2607.16862v2 Announce Type: replace Abstract: LiDAR place recognition supports loop closure, relocalization, and multi-agent map management. As robotic platforms increasingly combine LiDARs wit...