Lightweight Object-level Topological Semantic Mapping and Long-term Global Localization based on Graph Matching
Anhui Institute of Optics and Fine Mechanics, and Hefei Institutes of Physical Science, Chinese Academy of Sciences（中国科学院合肥物质科学研究院安徽光学精密机械研究所）；University of Science and Technology of China（中国科学技术大学）；National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所模式识别国家重点实验室）
Fan Wang, Chaofan Zhang, Fulin Tang, Hongkui Jiang, Yihong Wu, Yong Liu
Mapping and localization are two essential tasks for mobile robots in real-world applications. However, largescale and dynamic scenes challenge the accuracy and robustness of most current mature solutions. This situation becomes even worse when computational resources are limited. In this paper, we present a novel lightweight object-level mapping and localization method with high accuracy and robustness. Different from previous methods, our method does not need a prior constructed precise geometric map, which greatly releases the storage burden, especially for large-scale navigation. We use object-level features with both semantic and geometric information to model landmarks in the environment. Particularly, a learning topological primitive is first proposed to efficiently obtain and organize the object-level landmarks. On the basis of this, we use a robot-centric mapping framework to represent the environment as a semantic topology graph and relax the burden of maintaining global consistency at the same time. Besides, a hierarchical memory management mechanism is introduced to improve the efficiency of online mapping with limited computational resources. Based on the proposed map, the robust localization is achieved by constructing a novel local semantic scene graph descriptor, and performing multi-constraint graph matching to compare scene similarity. Finally, we test our method on a low-cost embedded platform to demonstrate its advantages. Experimental results on a large scale and multi-session real-world environment show that the proposed method outperforms the state of arts in terms of lightweight and robustness.
PRISM-Loc: a Lightweight Long-range LiDAR Localization in Urban Environments with Topological Maps
Federal Research Center for Computer Science and Control of Russian Academy of Sciences（俄罗斯科学院计算机科学与控制联邦研究中心）；Moscow Institute of Physics and Technology (MIPT)（莫斯科物理技术学院）；Artificial Intelligence Research Institute (AIRI)（人工智能研究所）；Innopolis University（Innopolis大学）
The Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
The core problem of visual multi-robot simultaneous localization and mapping (MR-SLAM) is how to efficiently and accurately perform multi-robot global localization (MR-GL). The difficulties are two-fold. The first is the difficulty of global localization for significant viewpoint difference. Appearance-based localization methods tend to fail under large viewpoint changes. Recently, semantic graphs have been utilized to overcome the viewpoint variation problem. However, the methods are highly time-consuming, especially in large-scale environments. This leads to the second difficulty, which is h…
TreeLoc: 6-DoF LiDAR Global Localization in Forests via Inter-Tree Geometric Matching
Dept. of Mechanical Engineering, SNU（机械工程系，首尔国立大学）；Oxford Robotics Institute, University of Oxford（牛津大学机器人研究所）；CSIRO Robotics, DATA61, CSIRO and the School of Electrical Engineering and Computer Science, The University of Queensland (UQ)（CSIRO机器人、DATA61、CSIRO及昆士兰大学电气工程与计算机科学学院）
The LIght Detection And Ranging (LiDAR) sensor has become one of the most important perceptual devices due to its important role in simultaneous localization and mapping (SLAM). Existing SLAM methods are mainly developed for mechanical LiDAR sensors, which are often adopted by large scale robots. Recently, the solid-state LiDAR is introduced and becomes popular since it provides a cost-effective and lightweight solution for small scale robots. Compared to mechanical LiDAR, solid-state LiDAR sensors have higher update frequency and angular resolution, but also have smaller field of view (FoV), …
Shanghai Jiao Tong University（上海交通大学）；Institute of Medical Robotics（医疗机器人研究院）
Long-term scene changes present challenges to localization systems using a pre-built map. This paper presents a LiDAR-based system that can provide robust localization against those challenges. Our method starts with activation of a mapping process temporarily when global matching towards the pre-built map is unreliable. The temporary map will be merged onto the pre-built map for later localization runs once reliable matching is obtained again. We further integrate a LiDAR inertial odometry (LIO) to provide motion-compensated LiDAR scans and a reliable initial pose guess for the global matchin…
BEVPlace++: Fast, Robust, and Lightweight LiDAR Global Localization for Unmanned Ground Vehicles
College of Intelligence Science and Technology, National University of Defense Technology（智能科学与技术学院，国防科技大学）；Haomo.AI Technology Co., Ltd（哈莫人工智能技术有限公司）；Ningbo Innovation Center, Zhejiang University（宁波创新中心，浙江大学）；College of Instrument Science and Optoelectronics Engineering, Beihang University（仪器科学与光电工程学院，北航）；College of Information Science and Electronic Engineering, Zhejiang University（信息科学与电子工程学院，浙江大学）
Learning-Based Hierarchical Scene Graph Matching for Robot Localization Leveraging Prior Maps
Automation and Robotics Research Group, Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg（卢森堡大学自动化与机器人研究组，安全、可靠性与信任跨学科研究中心（SnT））
This paper deals with the development of a localization methodology for autonomous vehicles using only a $3\Dim$ LIDAR sensor. In the context of this paper, localizing a vehicle in a known 3D global map of the environment is essentially to find its global $3\Dim$ pose (position and orientation) within this map. The problem of tracking is then to use sequential LIDAR scan measurement to also estimate other states such as velocity and angular rates, in addition to the global pose of the vehicle. Particle filters are often used in localization and tracking, as in applications of simultaneously lo…
Simultaneous localization and mapping (SLAM) has been a hot research field in the past years. Against the backdrop of more affordable 3D LiDAR sensors, research on 3D LiDAR SLAM is becoming increasingly popular. Furthermore, the re-localization problem with a point cloud map is the foundation for other SLAM applications. In this paper, a template matching framework is proposed to re-localize a robot globally in a 3D LiDAR map. This presents two main challenges. First, most global descriptors for point cloud can only be used for place detection under a small local area. Therefore, in order to r…
ShanghaiTech University（上海科技大学）；Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences（中国科学院上海微系统与信息技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai Engineering Research Center of Intelligent Vision and Imaging（上海智能视觉与成像工程研究中心）
We present a novel framework for floor plan-based, full six degree-of-freedom LiDAR localization. Our approach relies on robust ceiling and ground plane detection, which solves part of the pose and supports the segmentation of vertical structure elements such as walls and pillars. Our core contribution is a novel nearest neighbour data structure for an efficient look-up of nearest vertical structure elements from the floor plan. The registration is realized as a pair-wise regularized windowed pose graph optimization. Highly efficient, accurate and drift-free long-term localization is demonstra…
University of Vigo（维戈大学）；University of Freiburg（弗莱堡大学）
In this paper we present a novel approach to global localization using an RGB-D camera in maps of visual features. For large maps, the performance of pure image matching techniques decays in terms of robustness and computational cost. Particularly, repeated occurrences of similar features due to repeating structure in the world (e.g., doorways, chairs, etc.) or missing associations between observations pose critical challenges to visual localization. We address these challenges using a two-step approach. We first estimate a candidate pose using few correspondences between features of the curre…
Fast and Robust Localization for Humanoid Soccer Robot via Iterative Landmark Matching
Robotics and Mechanisms Laboratory (RoMeLa), Department of Mechanical and Aerospace Engineering, University of California, Los Angeles（机器人与机制实验室（RoMeLa）、机械与航空航天工程系、加州大学洛杉矶分校）
This paper proposes a novel inertial-aided localization approach by fusing information from multiple inertial measurement units (IMUs) and exteroceptive sensors. IMU is a low-cost motion sensor which provides measurements on angular velocity and gravity compensated linear acceleration of a moving platform, and widely used in modern localization systems. To date, most existing inertial-aided localization methods exploit only one single IMU. While the single-IMU localization yields acceptable accuracy and robustness for different use cases, the overall performance can be further improved by usin…
Robert Bosch GmbH（罗伯特·博世有限公司）；Goethe University（歌德大学）；Norwegian Open AI Lab（挪威开放人工智能实验室）；NTNU（挪威科技大学）
Ground texture based localization is a promising approach to achieve high-accuracy positioning of vehicles. We present a self-contained method that can be used for global localization as well as for subsequent local localization updates, i.e. it allows a robot to localize without any knowledge of its current whereabouts, but it can also take advantage of a prior pose estimate to reduce computation time significantly. Our method is based on a novel matching strategy, which we call identity matching, that is based on compact binary feature descriptors. Identity matching treats pairs of features …
Robust Subpixel Localization of Diagonal Markers in Large-Scale Navigation via Multi-Layer Screening and Adaptive Matching
College of Aerospace Science and Engineering（航空航天科学与工程学院）；National University of Defense Technology（国防科技大学）；Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation（湖南省图像测量与视觉导航重点实验室）
Proprioceptive localization refers to a new class of robot egocentric localization methods that do not rely on the perception and recognition of external landmarks. These methods are naturally immune to bad weather, poor lighting conditions, or other extreme environmental conditions that may hinder exteroceptive sensors such as a camera or a laser ranger finder. These methods depend on proprioceptive sensors such as inertial measurement units (IMUs) and/or wheel encoders. Assisted by magnetoreception, the sensors can provide a rudimentary estimation of vehicle trajectory which is used to query…
StereoVAE: A lightweight stereo-matching system using embedded GPUs
School of Computing, Tokyo Institute of Technology（东京技术大学计算机学系）；School of Electronic Science & Engineering, Nanjing University（南京大学电子科学与工程学院）；Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST)（国家先进工业科学与技术研究院人工智能研究中心）
ETH Zürich（苏黎世联邦理工学院）；Microsoft（微软）；Chalmers University of Technology（查尔姆斯理工大学）
Visual localization, i.e., determining the position and orientation of a vehicle with respect to a map, is a key problem in autonomous driving. We present a multicamera visual inertial localization algorithm for large scale environments. To efficiently and effectively match features against a pre-built global 3D map, we propose a prioritized feature matching scheme for multi-camera systems. In contrast to existing works, designed for monocular cameras, we (1) tailor the prioritization function to the multi-camera setup and (2) run feature matching and pose estimation in parallel. This signific…
MSG-Loc: Multi-Label Likelihood-based Semantic Graph Matching for Object-Level Global Localization
Electrical and Computer Engineering, Inha University（信息与通信技术规划与评估机构，延世大学）；Depart. of AI Machinery, Korea Institute of Machinery and Materials（人工智能机械系，韩国机械材料研究院）
Localization is one of the most important technologies needed to use Unmanned Aerial Vehicles (UAVs) in actual fields. Currently, most UAVs use GNSS to estimate their position. Recently, there have been attacks that target the weaknesses of UAVs that use GNSS, such as interrupting GNSS signal to crash the UAVs or sending fake GNSS signals to hijack the UAVs. To avoid this kind of situation, this paper proposes an algorithm that deals with the localization problem of the UAV in GNSS-denied environments. We propose a localization method, named as BRM (Building Ratio Map based) localization, for …
We present a real-time approach for image-based localization within large scenes that have been reconstructed offline using structure from motion (Sfm). From monocular video, our method continuously computes a precise 6-DOF camera pose, by efficiently tracking natural features and matching them to 3D points in the Sfm point cloud. Our main contribution lies in efficiently interleaving a fast keypoint tracker that uses inexpensive binary feature descriptors with a new approach for direct 2D-to-3D matching. The 2D-to-3D matching avoids the need for online extraction of scale-invariant features. …
In visual robot self-localization, graph-based scene representation and matching have recently attracted research interest as robust and discriminative methods for selflocalization. Although effective, their computational and storage costs do not scale well to large-size environments. To alleviate this problem, we formulate self-localization as a graph classification problem and attempt to use the graph convolutional neural network (GCN) as a graph classification engine. A straightforward approach is to use visual feature descriptors that are employed by state-of-the-art self-localization syst…
Risk-aware urban localization with the Global Navigation Satellite System (GNSS) remains an unsolved problem with frequent misdetection of the user's street or side of the street. Significant advances in 3D map-aided GNSS use grid-based GNSS shadow matching alongside AI-driven line-of-sight (LOS) classifiers and server-based processing to improve localization accuracy, especially in the cross-street direction. Our prior work introduces a new paradigm for shadow matching that proposes set-valued localization with computationally efficient zonotope set representations. While existing literature …
With the deepening of research on the SLAM system, the possibility of cooperative SLAM with multi-robots has been proposed. This paper presents a map matching and localization approach considering the cooperative SLAM of an aerial-ground system. The proposed approach aims to help precisely matching the map constructed by two independent systems that have large scale variance of viewpoints of the same route and eventually enables the ground mobile robot to localize itself in the global map given by the drone. It contains dense mapping with Elevation Map and software "Metashape", map matching wi…
LORIA-INRIA Lorraine（洛里亚-洛林因里亚研究所）；LAGIS-CNRS UMR 8146 Polytech'Lille（里尔理工大学LAGIS-CNRS联合研究所UMR 8146）
This paper presents a multi-sensor fusion strategy for a novel road-matching method designed to support real-time navigational features within advanced driving-assistance systems. Managing multihypotheses is a useful strategy for the road-matching problem. The multi-sensor fusion and multi-modal estimation are realized using Dynamical Bayesian Network. Experimental results, using data from Antilock Braking System (ABS) sensors, a differential Global Positioning System (GPS) receiver and an accurate digital roadmap, illustrate the performances of this approach, especially in ambiguous situation…
Stanford University（斯坦福大学）；University of Illinois, Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Indian Institute of Technology, Bombay（印度理工学院孟买分校）；University of Michigan - Ann Arbor（密歇根大学安娜堡分校）；Georgia Institute of Technology（佐治亚理工学院）
Unlike many urban localization methods that return point-valued estimates, a set-valued representation enables robustness by ensuring that a continuum of possible positions obeys safety constraints. One strategy with the potential for set-valued estimation is GNSS-based shadow matching~(SM), where one uses a three-dimensional (3-D) map to compute GNSS shadows (where line-of-sight is blocked). However, SM requires a point-valued grid for computational tractability, with accuracy limited by grid resolution. We propose zonotope shadow matching (ZSM) for set-valued 3-D map-aided GNSS localization.…
Georgia Institute of Technology（佐治亚理工学院）；Virginia Tech（弗吉尼亚理工学院暨州立大学）；University of Maryland at College Park（马里兰大学帕克分校）
Cross-view matching refers to the problem of finding the closest match for a given query ground view image to one from a database of aerial images. If the aerial images are geotagged, then the closest matching aerial image can be used to localize the query ground view image. Due to the recent success of deep learning methods, several cross-view matching techniques have been proposed. These approaches perform well for the matching of isolated query images. However, their evaluation over a trajectory is limited. In this paper, we evaluate cross-view matching for the task of localizing a ground v…
Most of the existing mobile robot localization solutions are either heavily dependent on pre-installed infrastructures or having difficulty working in highly repetitive environments which do not have sufficient unique features. To address this problem, we propose a magnetic-assisted initialization approach that enhances the performance of infrastructure-free mobile robot localization in repetitive featureless environments. The proposed system adopts a coarse-to-fine structure, which mainly consists of two parts: magnetic field-based matching and laser scan matching. Firstly, the interpolated m…
SLAMSpoof: Practical LiDAR Spoofing Attacks on Localization Systems Guided by Scan Matching Vulnerability Analysis
Keio University（庆应义塾大学）；the National Institute of Advanced Industrial Science and Technology（独立行政法人产业技术综合研究所）；University of California, Irvine（加利福尼亚大学欧文分校）；Amano Institute of Technology（天野工业大学）
Lightweight Learning from Actuation-Space Demonstrations via Flow Matching for Whole-Body Soft Robotic Grasping
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学）；University of Science and Technology of China (USTC)（中国科学技术大学）；University of Freiburg（弗莱堡大学）；Ludwig Maximilian University of Munich (LMU Munich)（慕尼黑大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
