Robotics and Sensor-Based Localization

Últimos artículos

• How to Reduce Localization Ambiguity? Geometry-Semantic Constrained BEV Representation Learning for Satellite-Ground Localization

  Junming Feng, Panwang Xia, Qiong Wu, Xudong Lu, Zeyu Jiao, Kun Lv, Zherong Wu, Yi Wan, Peifeng Ma, Li-Ta Hsu, Zhi Zheng · 1 de octubre de 2026

  Satellite-ground localization estimates the planar position and yaw orientation of a ground camera within a geo-referenced satellite image. Most recent methods map ground and satellite features into a shared bird's-eye-view (BEV) space and establish spatial correspondences. However, insufficient dep…

• GRC-Pose: Generation-Reconstruction Correspondence for Prior-Free 6D Object Pose Tracking

  Shiyang Liu, Weiquan Lin, Luping Xiao, Jiadong Tang, Yi Yang, Yu Gao, Xingyu Chen · 1 de octubre de 2026

  Prior-free 6D object pose tracking seeks to recover the trajectory of an unseen object from a single RGB video without object-specific CAD models, posed reference images, or pose annotations. Geometric foundation models provide complementary object-centric and scene-centric cues, yet SAM3D CAD is in…

• Agentic Relative Camera Pose Estimation via Learned Ranking and Verification

  Zhining Gu, Shangjie Du, Weimin Qiu, Carl Olsson, Ping Liu, Meng Tang · 1 de octubre de 2026

  A wide range of approaches have been developed for camera pose estimation, including correspondence-based methods, end-to-end pose regression, and recent 3D geometric foundation models. Our key observation is that no single estimator is optimal for diverse challenges, such as wide baselines, lack of…

• BatSLAM 2.0: Sequence-Verified Sonar Place Recognition in a Robust Pose Graph

  Jan Steckel · 1 de octubre de 2026

  Echolocating bats can navigate dark and cluttered spaces using echolocation. Over a decade ago, BatSLAM showed that a robot with a biomimetic binaural sonar can build a topological map of the environment, by recognizing places from the received acoustic signals. Sonar place recognition, however, is …

• A Differentiable Optimization Framework for Registering Sequential Bounding Boxes with Point Cloud Stream

  Xuesong Li, Jinguang Tong, Jie Hong · 29 de septiembre de 2026

  Refining a sequence of coarse 3D bounding boxes against a LiDAR point-cloud stream demands tracks that are geometrically accurate (high IoU) and temporally coherent (low roughness), preferably without training data. The usual recipe keeps the two concerns apart: register each frame independently, th…

• ChronoFuseGS: Multi-Temporal Gaussian Fusion with Per-Splat Persistence and Change Visualization

  Tobias Batik, Diana Marin, Peter K\'an, Hannes Kaufmann · 28 de septiembre de 2026

  Reconstructing environments where parts of the scene change between captured image sets poses a challenge for 3D scene reconstruction. We present ChronoFuseGS, a multi-temporal Gaussian Splatting approach that addresses this issue by taking multiple separately trained Gaussian Splatting models, each…

• VkVIO: Cross-platform GPU Acceleration for Visual-Inertial Odometry with Vulkan

  Ole Hoffmann, Mateo de Mayo, Daniel Cremers · 28 de septiembre de 2026

  Perception in robotics and XR fundamentally relies on good state estimation. Visual-inertial odometry (VIO) and Simultaneous Localization and Mapping (VI-SLAM) are proven ways of achieving this goal in a cost-effective and accurate manner. Efficiency in these systems allows for smaller, cooler, and …

• TRACKGRAPH: Online Open-Vocabulary 3D Scene Graphs via Image-Space Tracking

  Peder Borge Hellesylt, Albert Gassol Puigjaner, Kostas Alexis, Annette Stahl · 28 de septiembre de 2026

  Open-vocabulary 3D maps enable robots to reason about previously unknown environments using natural language. However, existing systems typically segment every incoming image, associate detections with persistent 3D segments, and frequently perform costly Vision-Language (VL) inference. We present T…

• DAPEVO: Deep Adaptive Patch Frame-Event Visual Odometry

  Luca Gandolfi, Simone Nascivera, Roberto Pellerito, Rong Zou, Chiara Plizzari, Davide Scaramuzza · 28 de septiembre de 2026

  Visual odometry is essential for autonomous navigation in GPS-denied environments, yet RGB-based methods remain vulnerable to motion blur, challenging illumination, and dropped frames. Event cameras complement conventional cameras with high temporal resolution and dynamic range, but their asynchrono…

• Reliability-Regulated Trajectory Optimization for Progressive COLMAP-Free 3D Gaussian Splatting

  Zijian Wu, Jinliang Wang, Zidian Lin, Ying Song, Ziqian Lu, Hanjie Ma, Zhen Ye, Mingfeng Jiang · 28 de septiembre de 2026

  COLMAP-free 3D Gaussian Splatting (3DGS) bypasses computationally expensive structure-from-motion (SfM) pipelines, yet progressive camera pose tracking remains fundamentally vulnerable to error compounding---early pairwise tracking inaccuracies both corrupt subsequent frame initializations and remai…

• PARTE: Plane-Assisted Robust Transformation Estimation for Point Cloud Registration

  Abolfazl Babanazari, Carson Cramer, Tyler Summers, Carlos Nieto, Kaveh Fathian · 25 de septiembre de 2026

  Global point-cloud registration remains challenging when limited overlap, repetitive geometry, and sensor noise produce correspondence sets dominated by outliers. Planar regions are particularly difficult for conventional point descriptors and are therefore often suppressed or discarded before match…

• Free-Init: Scan-Free, Motion-Free, and Correspondence-Free Initialization for Doppler LiDAR-Inertial Systems

  Mingle Zhao, Jiahao Wang, Tianxiao Gao, Chengzhong Xu, Hui Kong · 25 de septiembre de 2026

  Robust initialization is crucial for online systems. In the letter, a high-frequency and resilient initialization framework is designed for LiDAR-inertial systems, leveraging both inertial sensors and Doppler LiDAR. The innovative FMCW Doppler LiDAR opens up a novel avenue for robotic sensing by cap…

• FMCW-LIO: A Doppler LiDAR-Inertial Odometry

  Mingle Zhao, Jiahao Wang, Tianxiao Gao, Chengzhong Xu, Hui Kong · 25 de septiembre de 2026

  Conventional LiDAR-inertial odometry (LIO) or simultaneous localization and mapping (SLAM) methods heavily rely on geometric features of environments, as LiDARs primarily provide range measurements instead of motion measurements. From now on, however, the situation changes thanks to the novel Freque…

• UpDown-SC: Gravity-Canonicalized Dual-Envelope Scan Context for Indoor LiDAR Place Recognition

  Jie Xu, Yongxin Yang, Ziyi Jin, Kangjin Yu, Hongjun Huang, Chao Han, Zhongpu Xia · 25 de septiembre de 2026

  LiDAR place recognition is a key front end for loop closure and global relocalization, yet indoor retrieval remains difficult when attitude or sensor mounting height changes between mapping and query sessions. Scan Context stores the maximum height in each polar cell; indoors, broad ceilings can sup…

• DAVIO: Dense Monocular-Inertial SLAM with Feed-Forward Initialization and Pose-Conditioned Mapping

  Jaafar Mahmoud, Arthur Movsesyan, Mikhail Iumanov, Sergey Kolyubin · 24 de septiembre de 2026

  A camera and an IMU are the minimal sensor setup for metric localization and dense mapping, yet classical visual--inertial filters must wait for parallax before they start and then retain only sparse landmarks. Feed-forward geometry models, in contrast, predict dense structure from a few images but …

• Laser-Tracker-Assisted Camera-to-Robot Calibration for Mobile Robots

  Jan A. Rudolph, \"Oyk\"u Kandemir, Markus Ulrich · 24 de septiembre de 2026

  We present a laser-tracker-assisted hand-eye calibration method for camera-equipped mobile robots. The method combines laser-tracker-based 3D metrology with camera-based 2D observations. Building on our previous laser-tracker-assisted camera-to-robot calibration method for ground-observing mobile ro…

• Know-Your-Scene (KYS)-SLAM: Hierarchical Semantic-Motion Priors for Feature Matching in Stereo Visual SLAM

  Preeti Chatterjee, Jin Lu, Jin Sun, Suchendra M. Bhandarkar · 24 de septiembre de 2026

  Stereo visual SLAM systems built on local descriptors suffer from semantic ambiguity, instance-level confusion, and independently moving objects, each corrupting data association and accumulating as trajectory drift. Prevailing semantic and dynamic SLAM methods address this through binary feature re…

• PosEviLoc: Position-Conditioned Spatial Evidence for Language-Based 3D Localization

  Tianyi Shang, Yike Shi, Zhenyu Li · 22 de septiembre de 2026

  Language-based 3D localization retrieves the point-cloud submap containing a target position from descriptions of nearby objects and their spatial relations. Existing methods typically compress queries and submaps into global descriptors, potentially obscuring object-level semantics and cross-descri…

• ZIL: Zero-shot Image-to-LiDAR Registration

  Zijun Li, Xiaotian Sun, Xuelun Shen, Yao Dai, Sheng Ao, Yangyang Shi, Jakob Engel, Zhipeng Cai, Cheng Wang · 22 de septiembre de 2026

  Image-to-LiDAR registration estimates the camera pose of an image with respect to a LiDAR point cloud. It has diverse applications in autonomous driving, robot navigation etc. However, state-of-the-art (SOTA) methods still 1) mostly assume same-frame inputs, struggling with the image and point cloud…

• Hierarchical Aggregation of Semantic Uncertainty in 3D Scene Graphs

  Carlos Cueto Zumaya, Iacopo Catalano, Wallace Moreira Bessa, Julio A. Placed · 22 de septiembre de 2026

  Open-vocabulary 3D Scene Graphs (3DSGs) ground each object node in a vision-language embedding, yet they record every entry as equally certain, so a robot querying the map cannot tell which of its entries are unreliable. Estimators of semantic uncertainty could supply that distinction, but they requ…

• Refining Ground Truth Poses in Autonomous Driving Datasets via Neural Rendering

  Quentin Herau, Nathan Piasco, Moussab Bennehar, Luis Rold\~ao, Dzmitry Tsishkou, Bingbing Liu, Cyrille Migniot, Pascal Vasseur, C\'edric Demonceaux · 21 de septiembre de 2026

  Public autonomous driving datasets underpin the training and benchmarking of perception, mapping, and localization algorithms, yet residual inaccuracies in sensor calibration and ego-poses can silently degrade both model performance and evaluation reliability. We introduce MOISST++, a Neural Radianc…

• Robust Structureless Monocular Visual Inertial Initialization Exploiting Line Features and Vanishing Points

  Junwan Choi, Woongrae Jo, Dong-Uk Seo, Jinwoo Jeon, Hyun Myung · 21 de septiembre de 2026

  Accurate initialization is essential for reliable visual-inertial odometry (VIO), but it is often ill-conditioned under degenerate motions. Existing methods typically require restrictive excitation motions to ensure sufficient observability or rely on computationally expensive 3D structure reconstru…

• VideoReloc: Long-Term Indoor Video Relocalization against a Kilobyte-Scale Semantic Scene Graph

  Qianru Li, Xuyang Chen, Xuqin Wang, Zhenghao Zhang, Hongyi Luo, Tao Wu, Daniel Cremers, Lu Liu, Yanfeng Zhang · 21 de septiembre de 2026

  Given a compact semantic scene graph, long-term indoor video relocalization estimates a map-frame trajectory after lighting and furniture changes. Visual methods rely on appearance and become unreliable under these changes; localizing one frame at a time from object classes and geometry instead leav…

• XCalib Depth-Guided Geometric Optimization for Dense Thermal-Visible Video Registration

  Aurelien Godet, Gabriel Jobert, Mauro Dalla Mura · 21 de septiembre de 2026

  Image registration is a vital preprocessing step in multimodal perception tasks, including image fusion, object detection, and semantic segmentation. In Advanced Driver- Assistance Systems (ADAS), spatial misalignment between visible (RGB) and infrared (IR) cameras -caused by non-coincident optical …

• SFVO: Decoupled Confidence-Guided Stereo-Flow Visual Odometry with Bidirectional PnP

  Kai Zhang, Guoyang Zhao, Jun Ma · 21 de septiembre de 2026

  Deep learning-based visual odometry (VO) has achieved significant progress, yet most existing methods focus on a monocular approach, which suffers from scale ambiguity. Stereo VO provides real metric by its nature, but remains less studied in deep learning VO due to its high computational cost and m…