This work presents a systematic investigation into how alternative LiDAR-to-image projections affect metric place recognition when coupled with a state-of-the-art vision foundation model. We introduce a modular retrieval pipeline that controls for backbone, aggregation, and evaluation protocol, thereby isolating the influence of the 2-D projection itself. Using consistent geometric and structural channels across multiple datasets and deployment scenarios, we identify the projection characteristics that most strongly determine discriminative power, robustness to environmental variation, and suitability for real-time autonomy. Experiments with different datasets, including integration into an operational place recognition policy, validate the practical relevance of these findings and demonstrate that carefully designed projections can serve as an effective surrogate for end-to-end 3-D learning in LiDAR place recognition.
Real-time six degree-of-freedom pose estimation with ground vehicles represents a relevant and well studied topic in robotics, due to its many applications, such as autonomous driving and 3D mapping. Although some systems exist already, they are either not accurate or they struggle in real-time setting. In this paper, we propose a fast, accurate and modular LiDAR SLAM system for both batch and online estimation. We first apply downsampling and outlier removal, to filter out noise and reduce the size of the input point clouds. Filtered clouds are then used for pose tracking and floor detection,…
SNAIL Radar: A large-scale diverse benchmark for evaluating 4D-radar-based SLAM
Wuhan University（武汉大学）；The state key Lab of Information Engineering in Surveying, Mapping And Remote Sensing (LIESMARS), Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）；Wuhan Quantum Institute（武汉量子研究院）
LiDAR-based localization and mapping is one of the core components in many modern robotic systems due to the direct integration of range and geometry, allowing for precise motion estimation and generation of high quality maps in real-time. Yet, as a consequence of insufficient environmental constraints present in the scene, this dependence on geometry can result in localization failure, happening in self-symmetric surroundings such as tunnels. This work addresses precisely this issue by proposing a neural network-based estimation approach for detecting (non-)localizability during robot operati…
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS)（测绘遥感信息工程国家重点实验室）；Wuhan University（武汉大学）；UCLA Samueli School of Engineering（加州大学洛杉矶分校塞缪利工程学院）
Recent works recognized lidars as an inherently streaming data source and showed that the end-to-end latency of lidar perception models can be reduced significantly by operating on wedge-shaped point cloud sectors rather then the full point cloud. However, due to use of cartesian coordinate systems these methods represent the sectors as rectangular regions, wasting memory and compute. In this work we propose using a polar coordinate system and make two key improvements on this design. First, we increase the spatial context by using multi-scale padding from neighboring sectors: preceding sector…
Panoptic segmentation presents a new challenge in exploiting the merits of both detection and segmentation, with the aim of unifying instance segmentation and semantic segmentation in a single framework. However, an efficient solution for panoptic segmentation in the emerging domain of LiDAR point cloud is still an open research problem and is very much under-explored. In this paper, we present a fast and robust LiDAR point cloud panoptic segmentation framework, referred to as Panoptic-PolarNet. We learn both semantic segmentation and class-agnostic instance clustering in a single inference ne…
Modern high-definition LIDAR is expensive for commercial autonomous driving vehicles and small indoor robots. An affordable solution to this problem is fusion of planar LIDAR with RGB images to provide a similar level of perception capability. Even though state-of-the-art methods provide approaches to predict depth information from limited sensor input, they are usually a simple concatenation of sparse LIDAR features and dense RGB features through an end-to-end fusion architecture. In this paper, we introduce an inductive late-fusion block which better fuses different sensor modalities inspire…
PARTNER: Level up the Polar Representation for LiDAR 3D Object Detection
Fudan University（复旦大学）；National University of Singapore（新加坡国立大学）；Huawei International Pte Ltd（华为国际私人有限公司）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；Huawei ADS（华为自动驾驶解决方案部）
LiDARDustX: A LiDAR Dataset for Dusty Unstructured Road Environments
Wuxi Intelligent Control Research Institute,HNU（无锡智能控制研究院，湖南大学）；Hunan University（湖南大学）；Tsinghua University（清华大学）；Xi’an Jiaotong-Liverpool University（西安交通大学利物浦大学）
Aalto University（阿尔托大学）；University of Bonn（波恩大学）；University of Oxford（牛津大学）
Robust and accurate localization is a basic requirement for mobile autonomous systems. Pole-like objects, such as traffic signs, poles, and lamps are frequently used landmarks for localization in urban environments due to their local distinctiveness and long-term stability. In this paper, we present a novel, accurate, and fast pole extraction approach based on geometric features that runs online and has little computational demands. Our method performs all computations directly on range images generated from 3D LiDAR scans, which avoids processing 3D point clouds explicitly and enables fast po…
Evaluating the Impact of Weather-Induced Sensor Occlusion on BEVFusion for 3D Object Detection
Dept. of Electronic and Computer Engineering and the Data Driven Computer Engineering Research Centre, University of Limerick（电子与计算机工程系和数据驱动计算机工程研究中心，利默里克大学）；Valeo Vision Systems（瓦莱欧视觉系统）；Queen Mary University of London（伦敦女王大学）
University of Stuttgart（斯图加特大学）；Robert Bosch GmbH（罗伯特·博世有限公司）
Lidar sensors are costly yet critical for understanding the 3D environment in autonomous driving. High-resolution sensors provide more details about the surroundings because they contain more vertical beams, but they come at a much higher cost, limiting their inclusion in autonomous vehicles. Upsampling lidar pointclouds is a promising approach to gain the benefits of high resolution while maintaining an affordable cost. Although there exist many pointcloud upsampling frameworks, a consistent comparison of these works against each other on the same dataset using unified metrics is still missin…
School of Electrical Engineering and Computer Sciences, University of Ottawa（渥太华大学电气工程与计算机科学学院）；Sensorcortek Inc（森索科泰克公司）；Valeo（法雷奥公司）
Camera and Lidar processing have been revolutionized with the rapid development of deep learning model architectures. Automotive radar is one of the crucial elements of automated driver assistance and autonomous driving systems. Radar still relies on traditional signal processing techniques, unlike camera and Lidar based methods. We believe this is the missing link to achieve the most robust perception system. Identifying drivable space and occupied space is the first step in any autonomous decision making task. Occupancy grid map representation of the environment is often used for this purpos…
CLRNet: Targetless Extrinsic Calibration for Camera, Lidar and 4D Radar Using Deep Learning
Hungarian Research Network Institute for Computer Science and Control (HUN-REN SZTAKI)（匈牙利研究网络计算机科学与控制研究所）；Pázmány Péter Catholic University（帕兹曼·佩斯特天主教大学）；Intelligent Vehicles Group, TU Delft（智能车辆组，代尔夫特理工大学）；Perciv AI
LetsGo: Large-Scale Garage Modeling and Rendering via LiDAR-Assisted Gaussian Primitives
ShanghaiTech University（上海科技大学）；Shanghai Advanced Research Institute, Chinese Academy of Sciences（上海先进研究所，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；NeuDim Digital Technology (Shanghai) Co.,Ltd.（NeuDim数字技术（上海）有限公司）；DGene Digital Technology Co., Ltd.（DGene数字技术有限公司）
Robust Single Object Tracking in LiDAR Point Clouds under Adverse Weather Conditions
School of Mathematical Sciences, Dalian University of Technology（大连理工大学数学科学学院）；School of Economics and Management, China University of Mining and Technology（中国矿业大学经济管理学院）；DUT-BSU Joint Institute, Dalian University of Technology（大连理工大学-白俄罗斯国立大学联合学院）
Semantic segmentation of 3D point cloud data is essential for enhanced high-level perception in autonomous platforms. Furthermore, given the increasing deployment of LiDAR sensors onboard of cars and drones, a special emphasis is also placed on non-computationally intensive algorithms that operate on mobile GPUs. Previous efficient state-of-the-art methods relied on 2D spherical projection of point clouds as input for 2D fully convolutional neural networks to balance the accuracy-speed trade-off. This paper introduces a novel approach for 3D point cloud semantic segmentation that exploits mult…
Benchmarking Deep Learning Models for Aerial LiDAR Point Cloud Semantic Segmentation under Real Acquisition Conditions: A Case Study in Navarre
Department of Statistics, Computer Science and Mathematics and Institute of Smart Cities (ISC), Public University of Navarre (UPNA)（统计、计算机科学与数学系和智能城市研究所（ISC），纳瓦拉公共大学（UPNA））；Tracasa Instrumental
Active Learning (AL) has remained relatively unexplored for LiDAR perception tasks in autonomous driving datasets. In this study we evaluate Bayesian active learning methods applied to the task of dataset distillation or core subset selection (subset with near equivalent performance as full dataset). We also study the effect of application of data augmentation (DA) within Bayesian AL based dataset distillation. We perform these experiments on the full Semantic-KITTI dataset. We extend our study over our existing work only on 1/4th of the same dataset. Addition of DA and BALD have a negative im…
Steering Prediction via a Multi-Sensor System for Autonomous Racing
University of Burgundy（勃艮第大学）；Sensors Group, Institute of Neuroinformatics, UZH/ETH Zürich（苏黎世大学/苏黎世联邦理工学院神经信息学研究所传感器组）；University of Wurzburg（维尔茨堡大学）；Université Rouen Normandie（鲁昂诺曼底大学）；INSA Rouen Normandie（鲁昂诺曼底国立应用科学学院）；Université Le Havre Normandie（勒阿弗尔诺曼底大学）；Normandie Université（诺曼底大学联合体）；LITIS UR 4108（LITIS UR 4108实验室）
Tripura University（特里普拉大学）；Jadavpur University（贾达普大学）；Université Pierre et Marie Curie, Paris-6（皮埃尔和玛丽·居里大学（巴黎第六大学））；Université Denis Diderot, Paris-7（丹尼斯·狄德罗大学（巴黎第七大学））
This paper presents a comparative study of two different methods, which are based on fusion and polar transformation of visual and thermal images. Here, investigation is done to handle the challenges of face recognition, which include pose variations, changes in facial expression, partial occlusions, variations in illumination, rotation through different angles, change in scale etc. To overcome these obstacles we have implemented and thoroughly examined two different fusion techniques through rigorous experimentation. In the first method log-polar transformation is applied to the fused images …
Occluded nuScenes: A Multi-Sensor Dataset for Evaluating Perception Robustness in Automated Driving
University of Limerick（利默里克大学）；Data Driven Computer Engineering Research Centre（数据驱动计算机工程研究中心）；Lero, The Irish Software Research Centre（Lero爱尔兰软件研究中心）；Valeo Vision Systems（Valéo视觉系统）；Queen Mary University of London（伦敦女王学院）
HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation
Remote Sensing Technology Institute, German Aerospace Center (DLR)（远程传感技术研究所，德国航天中心）；Global Land Monitoring Group, Helmholtz German Research Centre for Geosciences (GFZ)（全球土地监测组，海德堡德国地理科学研究中心）；Department of Aerospace Engineering, University of the Bundeswehr Munich（航空航天工程系，联邦国防军 Munich 大学）；Chair of Data Science in Earth Observation (SiPEO), Technical University of Munich (TUM)（地球观测数据科学教授职位（SiPEO），慕尼黑技术大学）
