Robust localization in unstructured environments, such as agricultural fields, is a critical challenge for autonomous systems. LiDAR sensors provide detailed 3D information about the environment and are invariant to lighting conditions. For this reason, LiDAR-based place recognition methods have gained significant attention. In this paper, we propose MinkUNeXt-VINE++, a novel approach that combines early fusion of heterogeneous LiDAR data from two sensors (Livox Mid-360 and Velodyne VLP-16) and a learned re-ranking strategy in inference time. This fusion leverages the strengths of each sensor to provide a more comprehensive representation of the environment. Additionally, the re-ranking approach is particularly important in repetitive environments, such as vineyards, as finding true positives is a major challenge. We evaluated our approach using the TEMPO-VINE dataset, which provides heterogeneous LiDAR data in vineyard environments across different phenological stages. Our results demonstrate that MinkUNeXt-VINE++ significantly improves place recognition performance compared to single-sensor approaches and state-of-the-art methods. MinkUNeXt-VINE++ achieves a 20% improvement in the Recall@1 metric compared to single-sensor approaches, and +30% including re-ranking. The code of our method is publicly available for reproduction.
DATA61, CSIRO（联邦科学与工业研究组织DATA61）；Queensland University of Technology（昆士兰科技大学）
Many existing datasets for lidar place recognition are solely representative of structured urban environments, and have recently been saturated in performance by deep learning based approaches. Natural and unstructured environments present many additional challenges for the tasks of long-term localisation but these environments are not represented in currently available datasets. To address this we introduce Wild-Places, a challenging large-scale dataset for lidar place recognition in unstructured, natural environments. Wild-Places contains eight lidar sequences collected with a handheld senso…
German Aerospace Center (DLR)（德国航空航天中心（DLR））；Technical University of Munich（慕尼黑工业大学）
We present the DLR Planetary Stereo, Solid-State LiDAR, Inertial (S3LI) dataset, recorded on Mt. Etna, Sicily, an environment analogous to the Moon and Mars, using a hand-held sensor suite with attributes suitable for implementation on a space-like mobile rover. The environment is characterized by challenging conditions regarding both the visual and structural appearance: severe visual aliasing poses significant limitations to the ability of visual SLAM systems to perform place recognition, while the absence of outstanding structural details, joined with the limited Field-of-View of the utiliz…
Place recognition is a challenging problem in mobile robotics, especially in unstructured environments or under viewpoint and illumination changes. Most LiDAR-based methods rely on geometrical features to overcome such challenges, as generally scene geometry is invariant to these changes, but tend to affect camera-based solutions significantly. Compared to cameras, however, LiDARs lack the strong and descriptive appearance information that imaging can provide. To combine the benefits of geometry and appearance, we propose coupling the conventional geometric information from the LiDAR with its …
National Innovation Institute of Defence Technology, PLA Academy of Military Sciences（中国人民解放军军事科学院国防科技创新研究院）；PLA Information Engineering University（中国人民解放军信息工程大学）
Traditional LiDAR odometry (LO) systems mainly leverage geometric information obtained from the traversed surroundings to register laser scans and estimate LiDAR ego-motion, while it may be unreliable in dynamic or unstructured environments. This paper proposes InTEn-LOAM, a low-drift and robust LiDAR odometry and mapping method that fully exploits implicit information of laser sweeps (i.e., geometric, intensity, and temporal characteristics). Scanned points are projected to cylindrical images, which facilitate the efficient and adaptive extraction of various types of features, i.e., ground, b…
ETH Zurich（苏黎世联邦理工学院）；Voliro Airborne Robotics（Voliro空中机器人公司）
With the advent of powerful, light-weight 3D LiDARs, they have become the hearth of many navigation and SLAM algorithms on various autonomous systems. Pointcloud registration methods working with unstructured pointclouds such as ICP are often computationally expensive or require a good initial guess. Furthermore, 3D feature-based registration methods have never quite reached the robustness of 2D methods in visual SLAM. With the continuously increasing resolution of LiDAR range images, these 2D methods not only become applicable but should exploit the illumination-independent modalities that co…
Terrain-Awared LiDAR-Inertial Odometry for Legged-Wheel Robots Based on Radial Basis Function Approximation
School of Automation and Intelligent Sensing, Institute of Medical Robotics, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education（自动化与智能感知学院、医学机器人研究院、上海交通大学，系统控制与信息处理重点实验室）
Low Cost, High Efficiency: LiDAR Place Recognition in Vineyards with Matryoshka Representation Learning
University Institute for Engineering Research, Miguel Hernández University（工程研究院，米格尔·赫尔内亚大学）；Department of Electronics and Communications, Politecnico di Torino（电子与通信系，托里诺理工学院）
The University of Hong Kong（香港大学）；Southern University of Science and Technology（南方科技大学）
This paper proposes an efficient and probabilistic adaptive voxel mapping method for LiDAR odometry. The map is a collection of voxels; each contains one plane (or edge) feature that enables the probabilistic representation of the environment and accurate registration of a new LiDAR scan. We further analyze the need for coarse-to-fine voxel mapping and then use a novel voxel map organized by a Hash table and octrees to build and update the map efficiently. We apply the proposed voxel map to an iterated extended Kalman filter and construct a maximum a posteriori probability problem for pose est…
A Real-time Degeneracy Sensing and Compensation Method for Enhanced LiDAR SLAM
Wuhan University（武汉大学）；State Key Laboratory of Surveying, Mapping and Remote Sensing（测绘遥感信息工程国家重点实验室）；Beijing Automation Control Equipment Research Institute（北京自动化控制设备研究所）
The S3LI Vulcano Dataset: A Dataset for Multi-Modal SLAM in Unstructured Planetary Environments
German Aerospace Center (DLR) Institute of Robotics and Mechatronics（德国航空航天中心（DLR）机器人与机电研究所）；KTH Royal Institute of Technology（皇家理工学院）；Deptartment of Intelligent Systems（智能系统部门）
Vibration-aware Lidar-Inertial Odometry based on Point-wise Post-Undistortion Uncertainty
State Key Laboratory of Intelligent Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology（智能制造装备与技术国家重点实验室，机械科学与工程学院，华中科技大学）
Scalable Outdoors Autonomous Drone Flight with Visual-Inertial SLAM and Dense Submaps Built without LiDAR
Technical University of Munich（技术大学慕尼黑）；School of Computation, Information and Technology（计算、信息与技术学院）；Mobile Robotics Lab（移动机器人实验室）；Department of Computing（计算学院）；Imperial College London（伦敦帝国学院）；Department of Mechanical and Process Engineering（机械与过程工程学院）；ETH Zurich（苏黎世联邦理工学院）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
Matched Filtering based LiDAR Place Recognition for Urban and Natural Environments
Queensland University of Technology（昆士兰科技大学）；QUT Centre for Robotics（昆士兰科技大学机器人中心）；School of Electrical Engineering and Robotics（电气工程与机器人学院）；Intel Labs（英特尔实验室）
We propose a novel method for autonomous legged robot navigation in densely vegetated environments with a variety of pliable/traversable and non-pliable/untraversable vegetation. We present a novel few-shot learning classifier that can be trained on a few hundred RGB images to differentiate flora that can be navigated through, from the ones that must be circumvented. Using the vegetation classification and 2D lidar scans, our method constructs a vegetation-aware traversability cost map that accurately represents the pliable and non-pliable obstacles with lower, and higher traversability costs,…
AKF-LIO: LiDAR-Inertial Odometry with Gaussian Map by Adaptive Kalman Filter
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；International Digital Economy Academy（国际数字经济学院）；The Hong Kong University of Science and Technology（香港科技大学）；Southern University of Science and Technology（南方科技大学）
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）
We present a method for detecting and mapping trees in noisy stereo camera point clouds, using a learned 3-D object detector. Inspired by recent advancements in 3-D object detection using a pseudo-lidar representation for stereo data, we train a PointRCNN detector to recognize trees in forest-like environments. We generate detector training data with a novel automatic labeling process that clusters a fused global point cloud. This process annotates large stereo point cloud training data sets with minimal user supervision, and unlike previous pseudo-lidar detection pipelines, requires no 3-D gr…
FeatSense -- A Feature-based Registration Algorithm with GPU-accelerated TSDF-Mapping Backend for NVIDIA Jetson Boards
Osnabrück University（奥斯纳布吕克大学）；Fulda University of Applied Sciences（富尔达应用科学大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））
Computational Control Engineering Laboratory (CoCEL), Department of Convergence IT Engineering and Electrical Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, South Korea（convergence 信息技术工程与电气工程系 计算控制工程实验室，POSTECH 首尔大学（Pohang University of Science and Technology）, Pohang 37673, South Korea）；Laboratory for Information & Decision Systems (LIDS), Massachusetts Institute of Technology, Cambridge, MA 02139, USA（信息与决策系统实验室，麻省理工学院（Massachusetts Institute of Technology）, Cambridge, MA 02139, USA）
Ranking-aware Continual Learning for LiDAR Place Recognition
Shanghai Research Institute for Intelligent Autonomous System, Tongji University, Shanghai, China（上海智能自主系统研究院，同济大学）；Department of Computer Science and Technology, School of Electronics and Information Engineering, Tongji University, Shanghai, China（计算机科学与技术系，电子信息工程学院，同济大学）；MOE Key Lab of Embedded System and Service Computing, Tongji University, Shanghai, China（教育部嵌入式系统与服务计算重点实验室，同济大学）；Institute of Intelligent Vehicles, Tongji University, Shanghai, China（智能车辆研究院，同济大学）；SAIC Intelligent Technology (Shanghai) Co. Ltd, Shanghai, China（上汽智能科技（上海）有限公司）；College of Surveying and Geo-Informatics, Tongji University, Shanghai, China（测绘地理信息学院，同济大学）
Two core competencies of a mobile robot are to build a map of the environment and to estimate its own pose on the basis of this map and incoming sensor readings. To account for the uncertainties in this process, one typically employs probabilistic state estimation approaches combined with a model of the specific sensor. Over the past years, lidar sensors have become a popular choice for mapping and localization. However, many common lidar models perform poorly in unstructured, unpredictable environments, they lack a consistent physical model for both mapping and localization, and they do not e…
RoboLoc: A Benchmark Dataset for Point Place Recognition and Localization in Indoor-Outdoor Integrated Environments
Department of Data, Network and AI, Ajou University（数据、网络与人工智能系，亚运会大学）；Korea Railroad Research Institute（韩国铁路研究院）；Department of Mobility Engineering, Ajou University（移动工程系，亚运会大学）
Continuum Robot Localization using Distributed Time-of-Flight Sensors
University of Toronto Robotics Institute, Canada（多伦多大学机器人研究所，加拿大）；Oxford Robotics Institute, University of Oxford, UK（牛津大学机器人研究所，英国）；Toronto Metropolitan University, Canada（多伦多 Metropolitan 大学，加拿大）
Simultaneous localisation and mapping (SLAM) is the problem of autonomous robots to construct or update a map of an undetermined unstructured environment while simultaneously estimate the pose in it. The current trend towards self-driving vehicles has influenced the development of robust SLAM techniques over the last 30 years. This problem is addressed by using a standard sensor or a sensor array (Ultrasonic sensor, LIDAR, Camera, Kinect RGB-D) with sensor fusion techniques to achieve the perception step. Sensing method is determined by considering the specifications of the environment to extr…
South China Agricultural University（华南农业大学）；Monash University（莫纳什大学）；CSIRO（联邦科学与工业研究组织）
Semantic segmentation is a fundamental task for agricultural robots to understand the surrounding environments in natural orchards. The recent development of the LiDAR techniques enables the robot to acquire accurate range measurements of the view in the unstructured orchards. Compared to RGB images, 3D point clouds have geometrical properties. By combining the LiDAR and camera, rich information on geometries and textures can be obtained. In this work, we propose a deep-learning-based segmentation method to perform accurate semantic segmentation on fused data from a LiDAR-Camera visual sensor.…
A Graph Attention Network-Based Framework for Reconstructing Missing LiDAR Beams
Computer Information Technology Department, SUNY Morrisville（SUNY Morrisville 计算机信息科技系）；Electrical and Computer Engineering Department, Portland State University（波特兰州立大学电气与计算机工程系）；Electrical and Computer Engineering Department, Lawrence Technological University（劳伦斯技术大学电气与计算机工程系）
TE-NeXt: A LiDAR-Based 3D Sparse Convolutional Network for Traversability Estimation
University Institute for Engineering Research, Miguel Hernández University.（工程研究大学学院，米格尔·赫尔内亚大学）；Valencian Graduate School and Research Network of Artificial Intelligence (valgrAI)（瓦伦西亚人工智能研究生学院和研究网络）；Univ. of Coimbra, ADAI, Department of Mechanical Engineering（科英布拉大学，ADAI，机械工程系）
