GOReloc: Graph-based Object-Level Relocalization for Visual SLAM
School of Mechanical Engineering, Beijing Institute of Technology（北京理工大学机械工程学院）；Yangtze Delta Region Academy of Beijing Institute of Technology（北京理工大学长三角研究院）；College of Intelligence Science and Technology, National University of Defense Technology（国防科技大学智能科学学院）
This article introduces a novel method for object-level relocalization of robotic systems. It determines the pose of a camera sensor by robustly associating the object detections in the current frame with 3D objects in a lightweight object-level map. Object graphs, considering semantic uncertainties, are constructed for both the incoming camera frame and the pre-built map. Objects are represented as graph nodes, and each node employs unique semantic descriptors based on our devised graph kernels. We extract a subgraph from the target map graph by identifying potential object associations for each object detection, then refine these associations and pose estimations using a RANSAC-inspired strategy. Experiments on various datasets demonstrate that our method achieves more accurate data association and significantly increases relocalization success rates compared to baseline methods. The implementation of our method is released at \url{https://github.com/yutongwangBIT/GOReloc}.
Fast and Accurate Deep Loop Closing and Relocalization for Reliable LiDAR SLAM
College of Intelligence Science and Technology, National University of Defense Technology（国防科技大学智能科学与技术学院）；National Innovation Institution of Defense Technology（国防科技国家创新研究院）
本文提出统一框架 LCR-Net，通过特征提取和位姿感知注意力机制精确估计 LiDAR 扫描相似度与 6-DoF 位姿，实现快速准确的深度闭环与重定位，并集成至 SLAM 系统提升在线可靠性。
Instituto de Investigación en Ingeniería de Aragón (I3A)（阿拉贡工程研究院（I3A））；Universidad de Zaragoza（萨拉戈萨大学）
This paper presents ORB-SLAM, a feature-based monocular SLAM system that operates in real time, in small and large, indoor and outdoor environments. The system is robust to severe motion clutter, allows wide baseline loop closing and relocalization, and includes full automatic initialization. Building on excellent algorithms of recent years, we designed from scratch a novel system that uses the same features for all SLAM tasks: tracking, mapping, relocalization, and loop closing. A survival of the fittest strategy that selects the points and keyframes of the reconstruction leads to excellent r…
Midea Robozone Inc（美的机器人公司）；Massachusetts Institute of Technology（麻省理工学院）
Camera relocalization is the key component of simultaneous localization and mapping (SLAM) systems. This paper proposes a learning-based approach, named Sparse Spatial Scene Embedding with Graph Neural Networks (S3E-GNN), as an end-to-end framework for efficient and robust camera relocalization. S3E-GNN consists of two modules. In the encoding module, a trained S3E network encodes RGB images into embedding codes to implicitly represent spatial and semantic embedding code. With embedding codes and the associated poses obtained from a SLAM system, each image is represented as a graph node in a p…
For Lifelong SLAM, one has to deal with temporary localization failures, e.g., induced by kidnapping. We achieve this by starting a new map and merging it with the previous map as soon as relocalization succeeds. Since relocalization methods are fallible, it can happen that such a merge is invalid, e.g., due to perceptual aliasing. To address this issue, we propose methods to detect and undo invalid merges. These methods compare incoming scans with scans that were previously merged into the current map and consider how well they agree with each other. Evaluation of our methods takes place usin…
Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza（萨拉戈萨大学阿拉贡工程研究所（I3A））；Oculus Research
We present ORB-SLAM2 a complete SLAM system for monocular, stereo and RGB-D cameras, including map reuse, loop closing and relocalization capabilities. The system works in real-time on standard CPUs in a wide variety of environments from small hand-held indoors sequences, to drones flying in industrial environments and cars driving around a city. Our back-end based on bundle adjustment with monocular and stereo observations allows for accurate trajectory estimation with metric scale. Our system includes a lightweight localization mode that leverages visual odometry tracks for unmapped regions …
In this paper, we present the RISE-SLAM algorithm for performing visual-inertial simultaneous localization and mapping (SLAM), while improving estimation consistency. Specifically, in order to achieve real-time operation, existing approaches often assume previously-estimated states to be perfectly known, which leads to inconsistent estimates. Instead, based on the idea of the Schmidt-Kalman filter, which has processing cost linear in the size of the state vector but quadratic memory requirements, we derive a new consistent approximate method in the information domain, which has linear memory r…
Robotics Institute（机器人研究所）；University of Michigan（密歇根大学）
Deep learning based camera pose estimation from monocular camera images has seen a recent uptake in Visual SLAM research. Even though such pose estimation approaches have excellent results in small confined areas like offices and apartment buildings, they tend to do poorly when applied to larger areas like outdoor settings, mainly because of the scarcity of distinctive features. We propose GODSAC* as a camera pose estimation approach that augments pose predictions from a trained neural network with noisy odometry data through the optimization of a pose graph. GODSAC* outperforms the state-of-t…
University of South Carolina（南卡罗来纳大学）；Dartmouth College（达特茅斯学院）
This paper presents a novel tightly-coupled keyframe-based Simultaneous Localization and Mapping (SLAM) system with loop-closing and relocalization capabilities targeted for the underwater domain. Our previous work, SVIn, augmented the state-of-the-art visual-inertial state estimation package OKVIS to accommodate acoustic data from sonar in a non-linear optimization-based framework. This paper addresses drift and loss of localization -- one of the main problems affecting other packages in underwater domain -- by providing the following main contributions: a robust initialization method to refi…
Leveraging Semantic Graphs for Efficient and Robust LiDAR SLAM
College of Intelligence Science and Technology and the National Key Laboratory of Equipment State Sensing and Smart Support, National University of Defense Technology, China（智能科学与技术学院和装备状态感知与智能支撑国家重点实验室，国防科技大学，中国）；T Stone Robotics Institute and Department of Mechanical and Automation Engineering, the Chinese University of Hong Kong, China（石墨机器人研究院和机械与自动化工程系，香港中文大学，中国）
Sapienza University of Rome（罗马第一大学）；University of Bonn（波恩大学）；University of Oxford（牛津大学）
Simultaneous Localization and Mapping (SLAM) systems are fundamental building blocks for any autonomous robot navigating in unknown environments. The SLAM implementation heavily depends on the sensor modality employed on the mobile platform. For this reason, assumptions on the scene's structure are often made to maximize estimation accuracy. This paper presents a novel direct 3D SLAM pipeline that works independently for RGB-D and LiDAR sensors. Building upon prior work on multi-cue photometric frame-to-frame alignment, our proposed approach provides an easy-to-extend and generic SLAM system. …
Most real-time autonomous robot applications require a robot to traverse through a dynamic space for a long time. In some cases, a robot needs to work in the same environment. Such applications give rise to the problem of a life-long SLAM system. Life-long SLAM presents two main challenges i.e. the tracking should not fail in a dynamic environment and the need for a robust and efficient mapping strategy. The system should update maps with new information; while also keeping track of older observations. But, mapping for a long time can require higher computational requirements. In this paper, w…
In this work, we propose a simultaneous localization and mapping (SLAM) system using a monocular camera and Ultra-wideband (UWB) sensors. Our system, referred to as VRSLAM, is a multi-stage framework that leverages the strengths and compensates for the weaknesses of each sensor. Firstly, we introduce a UWB-aided 7 degree-of-freedom (scale factor, 3D position, and 3D orientation) global alignment module to initialize the visual odometry (VO) system in the world frame defined by the UWB anchors. This module loosely fuses up-to-scale VO and ranging data using either a quadratically constrained qu…
Recognizing already explored places (a.k.a. place recognition) is a fundamental task in Simultaneous Localization and Mapping (SLAM) to enable robot relocalization and loop closure detection. In topological SLAM the recognition takes place by comparing a signature (or feature vector) associated to the current node with the signatures of the nodes in the known map. However, as the number of nodes increases, matching the current node signature against all the existing ones becomes inefficient and thwarts real-time navigation. In this paper we propose a novel approach to pre-select a subset of ma…
ROVER: Robust Loop Closure Verification with Trajectory Prior in Repetitive Environments
Hong Kong University of Science and Technology（香港科技大学）；Shenzhen Key Laboratory of Robotics and Computer Vision（深圳机器人与计算机视觉重点实验室）；The University of Tokyo（东京大学）；Southern University of Science and Technology（南方科技大学）
SUPER -- A Framework for Sensitivity-based Uncertainty-aware Performance and Risk Assessment in Visual Inertial Odometry
Hertie Institute for Clinical Brain Research & Center for Integrative Neuroscience, University of Tübingen（图宾根大学赫尔蒂临床脑研究所在职机构及整合神经科学中心）；Intelligent Process Automation and Robotics Lab (IPR), Karlsruhe Institute of Technology (KIT), Germany, Artificial Intelligence Institute (AIIS), Seoul National University, Republic of Korea（卡尔斯鲁厄理工学院（KIT）智能过程自动化与机器人实验室（IPR）、人工智能研究所（AIIS）、首尔国立大学）
We propose a Visual Teach and Repeat (VTR) algorithm using semantic landmarks extracted from environmental objects for ground robots with fixed mount monocular cameras. The proposed algorithm is robust to changes in the starting pose of the camera/robot, where a pose is defined as the planar position plus the orientation around the vertical axis. VTR consists of a teach phase in which a robot moves in a prescribed path, and a repeat phase in which the robot tries to repeat the same path starting from the same or a different pose. Most available VTR algorithms are pose dependent and cannot perf…
DualVision ArthroNav: Investigating Opportunities to Enhance Localization and Reconstruction in Image-based Arthroscopy Navigation via External Cameras
University of Arkansas（阿肯色大学）；Arthrex Inc.（阿瑟克斯公司）
Deep Learning for Visual Localization and Mapping: A Survey
National University of Defense Technology（国防科技大学）；The Hong Kong Polytechnic University（香港理工大学）；University of Edinburgh（爱丁堡大学）；University of Oxford（牛津大学）
SAFEVPR: Patch-Based Conformal Verification for Safe Cross-Condition Sequence Visual Place Recognition
Turku Intelligent Embedded and Robotic Systems (TIERS) Lab（图尔库智能嵌入式与机器人系统实验室）；University of Turku（图尔库大学）；School of Information Science and Technology（信息科学与技术学院）
CCTNet: A Circular Convolutional Transformer Network for LiDAR-based Place Recognition Handling Movable Objects Occlusion
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；China North Vehicle Research Institute (NOVERI)（中国北方车辆研究所）；Centre for Composite Materials and Structures, Harbin Institute of Technology（哈尔滨工业大学复合材料与结构中心）；Faculty of Computing, Harbin Institute of Technology（哈尔滨工业大学计算学部）；National Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Harbin Institute of Technology（特殊环境先进复合材料科学与技术国家重点实验室）
We demonstrate the use of semantic object detections as robust features for Visual Teach and Repeat (VTR). Recent CNN-based object detectors are able to reliably detect objects of tens or hundreds of categories in a video at frame rates. We show that such detections are repeatable enough to use as landmarks for VTR, without any low-level image features. Since object detections are highly invariant to lighting and surface appearance changes, our VTR can cope with global lighting changes and local movements of the landmark objects. In the teaching phase, we build a series of compact scene descri…
