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.
By Minghang Zhu, Zhijing Wang, Yuxin Guo, Chen Liu, Yongshu Huang, Wen Li, Sheng Ao, Cheng Wang
arXiv:2609.25375v1 Announce Type: cross
Abstract: Global point-cloud registration remains challenging when limited overlap, repetitive geometry, and sensor noise produce correspondence sets dominated...
By Abolfazl Babanazari, Carson Cramer, Tyler Summers, Carlos Nieto, Kaveh Fathian
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...
By Zhixuan Chen, Jialiang Lu, Zhong Ye, Yinghui He, Guanding Yu
arXiv:2607. 08391v1 Announce Type: cross Abstract: Making tradeoffs between execution latency and result utility (i.
By Ahmet Soyyigit, Shuochao Yao, Heechul Yun
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...
By Nikolaos Stathoulopoulos, George Nikolakopoulos
arXiv:2506. 22784v2 Announce Type: replace-cross Abstract: Point-pixel registration between LiDAR point clouds and camera images is a fundamental yet challenging task in autonomous driving and robotic perception.
By Yu Han, Zhiwei Huang, Yanting Zhang, Fangjun Ding, Shen Cai, Xiaoyu Tang, Yanchao Dong, Rui Fan
DRS‑VPT is a feed‑forward transformer that performs image‑to‑scan registration by predicting the scan pose, point maps, and a coarse‑to‑fine feature pyramid for direct reprojective alignment. It unifies tasks like camera‑LiDAR calibration and indoor camera‑to‑map relocalization, achieving state‑of‑the‑art results in autonomous driving and competitive performance indoors without map‑specific training. The model also learns complex scan‑to‑image projection behaviors, such as occlusion of back‑facing points.
By Lanke Frank Tarimo Fu, Maurice Fallon
arXiv:2609.06195v1 Announce Type: new
Abstract: This paper proposes a transferable Map of Dynamics (MoD) framework that generalizes to unknown environments using only egocentric 3D LiDAR point clouds...
By Azusa Sawada, Allan Wang, Hideo Saito, Aaron Steinfeld
LiDAR-based Scene Coordinate Regression (SCR) maps point clouds directly to 3D scene coordinates, enabling precise 6-DoF localisation without explicit map retrieval. However, existing methods produce deterministic predictions, discarding aleatoric uncertainty that could improve robustness and downstream decision-making.
Metric scale monocular geometry estimation has seen significant progress through large-scale data aggregation, yet current foundation models suffer from a persistent ''scale-collapse'' phenomenon: distant landmarks and vast landscapes are metrically underestimated. We hypothesize that this performance gap stems from a training data bottleneck, where existing metric-scale datasets are hardware-constrained to homogenous vehicle-captured LiDAR or short-range indoor scans, or consist of synthetic data that lacks the semantic complexity of the physical world.
arXiv:2404.09431v3 Announce Type: replace
Abstract: Pseudo-LiDAR has become a promising paradigm for monocular 3D object detection by transforming monocular images into point cloud representations th...
By Bonan Ding, Jin Xie, Jing Nie, Jiale Cao, Yanwei Pang
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...