Modern autonomous vehicles (AVs) often rely on vision, LIDAR, and even radar-based simultaneous localization and mapping (SLAM) frameworks for precise localization and navigation. However, modern SLAM frameworks often lead to unacceptably high levels of drift (i.e., localization error) when AVs observe few visually distinct features or encounter occlusions due to dynamic obstacles. This paper argues that minimizing drift must be a key desiderata in AV motion planning, which requires an AV to take active control decisions to move towards feature-rich regions while also minimizing conventional control cost. To do so, we first introduce a novel data-driven perception module that observes LIDAR point clouds and estimates which features/regions an AV must navigate towards for drift minimization. Then, we introduce an interpretable model predictive controller (MPC) that moves an AV toward such feature-rich regions while avoiding visual occlusions and gracefully trading off drift and control cost. Our experiments on challenging, dynamic scenarios in the state-of-the-art CARLA simulator indicate our method reduces drift up to 76.76% compared to benchmark approaches.
Northwestern Polytechnical University（西北工业大学）；City University of London（伦敦城市大学）
Autonomous navigation in unknown complex environment is still a hard problem, especially for small Unmanned Aerial Vehicles (UAVs) with limited computation resources. In this paper, a neural network-based reactive controller is proposed for a quadrotor to fly autonomously in unknown outdoor environment. The navigation controller makes use of only current sensor data to generate the control signal without any optimization or configuration space searching, which reduces both memory and computation requirement. The navigation problem is modelled as a Markov Decision Process (MDP) and solved using…
IIIT-Hyderabad（国际信息技术研究所海得拉巴分校）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
We embark on a hitherto unreported problem of an autonomous robot (self-driving car) navigating in dynamic scenes in a manner that reduces its localization error and eventual cumulative drift or Absolute Trajectory Error, which is pronounced in such dynamic scenes. With the hugely popular Velodyne-16 3D LIDAR as the main sensing modality, and the accurate LIDAR-based Localization and Mapping algorithm, LOAM, as the state estimation framework, we show that in the absence of a navigation policy, drift rapidly accumulates in the presence of moving objects. To overcome this, we learn actions that …
Visual-inertial SLAM is essential for robot navigation in GPS-denied environments, e.g. indoor, underground. Conventionally, the performance of visual-inertial SLAM is evaluated with open-loop analysis, with a focus on the drift level of SLAM systems. In this paper, we raise the question on the importance of visual estimation latency in closed-loop navigation tasks, such as accurate trajectory tracking. To understand the impact of both drift and latency on visual-inertial SLAM systems, a closed-loop benchmarking simulation is conducted, where a robot is commanded to follow a desired trajectory…
Centre for Automation and Robotics, Polytechnic University of Madrid（马德里理工大学自动化与机器人中心）
This article proposes a method to diminish the pose (position plus attitude) drift experienced by an SVO (Semi-Direct Visual Odometry) based visual navigation system installed onboard a UAV (Unmanned Air Vehicle) by supplementing its pose estimation non linear optimizations with priors based on the outputs of a GNSS (Global Navigation Satellite System) Denied inertial navigation system. The method is inspired in a PI (Proportional Integral) control system, in which the attitude, altitude, and rate of climb inertial outputs act as targets to ensure that the visual estimations do not deviate far…
Untethered, inside-out tracking is considered a new goalpost for virtual reality, which became attainable with advent of machine learning in SLAM. Yet computer vision-based navigation is always at risk of a tracking failure due to poor illumination or saliency of the environment. An extension for a navigation system is proposed, which recognizes agents motion and stillness states with 87% accuracy from accelerometer data. 40% reduction in navigation drift is demonstrated in a repeated tracking failure scenario on a challenging dataset.
In the absence of external reference position information (e.g. GNSS) SLAM has proven to be an effective method for indoor navigation. The positioning drift can be reduced with regular loop-closures and global relaxation as the backend, thus achieving a good balance between exploration and exploitation. Although vision-based systems like laser scanners are typically deployed for SLAM, these sensors are heavy, energy inefficient, and expensive, making them unattractive for wearables or smartphone applications. However, the concept of SLAM can be extended to non-optical systems such as magnetome…
Inland-LOAM: Voxel-Based Structural Semantic LiDAR Odometry and Mapping for Inland Waterway Navigation
Division of Robotics, Automation and Mechatronics in the Department of Mechanical Engineering, KU Leuven（机械工程系机器人、自动化与机电系，KU莱顿大学）；Division of Declarative Languages and Artificial Intelligence (DTAI) in the Department of Computer Science, KU Leuven（计算机科学系声明语言与人工智能系（DTAI），KU莱顿大学）
Risky and crowded environments (RCE) contain abstract sources of risk and uncertainty, which are perceived differently by humans, leading to a variety of behaviors. Thus, robots deployed in RCEs, need to exhibit diverse perception and planning capabilities in order to interpret other human agents' behavior and act accordingly in such environments. To understand this problem domain, we conducted a study to explore human path choices in RCEs, enabling better robotic navigational explainable AI (XAI) designs. We created a novel COVID-19 pandemic grocery shopping scenario which had time-risk trade…
BIND-USBL: Bounding IMU Navigation Drift using USBL in Heterogeneous ASV-AUV Teams
Dept of Computer and Information Science, University of Konstanz, Germany（计算机与信息科学系，康斯坦茨大学，德国）；Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Germany（集体行为高级研究中心，康斯坦茨大学，德国）；Dept of Aerospace Engineering, Indian Institute of Science, Bangalore, India（航空航天工程系，印度科学研究院，班加罗尔，印度）
Wheel-GINS: A GNSS/INS Integrated Navigation System with a Wheel-mounted IMU
University of Bonn（波恩大学）；Institute of Geodesy and Geoinformation（大地测量与地理信息研究所）；Center for Robotics（机器人中心）；University of Oxford（牛津大学）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习与人工智能研究所）；GNSS Research Center（全球导航卫星系统研究中心）；Wuhan University（武汉大学）
HE Space for ESA – European Space Agency（HE Space 公司（为欧洲空间局））；European Space Agency（欧洲空间局）；University of Malaga（马拉加大学）
This paper proposes a Guidance, Navigation, and Control (GNC) architecture for planetary rovers targeting the conditions of upcoming Mars exploration missions such as Mars 2020 and the Sample Fetching Rover (SFR). The navigation requirements of these missions demand a control architecture featuring autonomous capabilities to achieve a fast and long traverse. The proposed solution presents a two-level architecture where the efficient navigation (low) level is always active and the full navigation (upper) level is enabled according to the difficulty of the terrain. The first level is an efficien…
Polytechnic University of Madrid（马德里理工大学）；Boeing Research & Technology Europe（波音欧洲研究与技术公司）；National University of Distance Education（国立远程教育大学）；Center for Automation and Robotics (UPM-CSIC)（自动化与机器人中心（UPM-CSIC））
This article proposes an inertial navigation algorithm intended to lower the negative consequences of the absence of GNSS (Global Navigation Satellite System) signals on the navigation of autonomous fixed wing low SWaP (Size, Weight, and Power) UAVs (Unmanned Air Vehicles). In addition to accelerometers and gyroscopes, the filter takes advantage of sensors usually present onboard these platforms, such as magnetometers, Pitot tube, and air vanes, and aims to minimize the attitude error and reduce the position drift (both horizontal and vertical) with the dual objective of improving the aircraft…
Nanyang Technological University（南洋理工大学）；Australian National University（澳大利亚国立大学）
Although Simultaneous Localization and Mapping (SLAM) has been an active research topic for decades, current state-of-the-art methods still suffer from instability or inaccuracy due to feature insufficiency or its inherent estimation drift, in many civilian environments. To resolve these issues, we propose a navigation system combing the SLAM and prior-map-based localization. Specifically, we consider additional integration of line and plane features, which are ubiquitous and more structurally salient in civilian environments, into the SLAM to ensure feature sufficiency and localization robust…
Technical University of Munich（慕尼黑工业大学）；German Aerospace Center（德国航空航天中心）
Monocular vision-based Simultaneous Localization and Mapping (SLAM) is used for various purposes due to its advantages in cost, simple setup, as well as availability in the environments where navigation with satellites is not effective. However, camera motion and map points can be estimated only up to a global scale factor with monocular vision. Moreover, estimation error accumulates over time without bound, if the camera cannot detect the previously observed map points for closing a loop. We propose an innovative approach to estimate a global scale factor and reduce drifts in monocular vision…
Design and Implementation of an Inertial Navigation System for Pedestrians Based on a Low-Cost MEMS IMU
Department of Engineering ``Enzo Ferrari'', University of Modena（工程系（恩佐·费拉里），莫德纳大学）；Department of Science and Methods for Engineering, University of Modena（工程科学与方法系，莫德纳大学）
Immersive Explainability: Visualizing Robot Navigation Decisions through XAI Semantic Scene Projections in Virtual Reality
University of Bonn（波恩大学）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔人工智能与机器学习研究所）；Center for Robotics（机器人中心）；University of Mainz（美因茨大学）；Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS（弗劳恩霍夫智能分析与信息系统研究所）
Delft University of Technology（代尔夫特理工大学）；University of Liverpool（利物浦大学）
This paper presents a literature survey and a comparative study of Bug Algorithms, with the goal of investigating their potential for robotic navigation. At first sight, these methods seem to provide an efficient navigation paradigm, ideal for implementations on tiny robots with limited resources. Closer inspection, however, shows that many of these Bug Algorithms assume perfect global position estimate of the robot which in GPS-denied environments implies considerable expenses of computation and memory -- relying on accurate Simultaneous Localization And Mapping (SLAM) or Visual Odometry (VO)…
Motivated by the goal of achieving long-term drift-free camera pose estimation in complex scenarios, we propose a global positioning framework fusing visual, inertial and Global Navigation Satellite System (GNSS) measurements in multiple layers. Different from previous loosely- and tightly- coupled methods, the proposed multi-layer fusion allows us to delicately correct the drift of visual odometry and keep reliable positioning while GNSS degrades. In particular, local motion estimation is conducted in the inner-layer, solving the problem of scale drift and inaccurate bias estimation in visual…
Long Distance GNSS-Denied Visual Inertial Navigation for Autonomous Fixed Wing Unmanned Air Vehicles: SO(3) Manifold Filter based on Virtual Vision Sensor
FloorPlan-VLN: A New Paradigm for Floor Plan Guided Vision-Language Navigation
New Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences and School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院自动化研究所新型模式识别实验室，中国科学院人工智能学院）；AMap, Alibaba Group（阿里巴巴集团高德地图）；BAAI（北京人工智能研究院）；Xiaomi Robotics Lab（小米机器人实验室）；FiveAges
Learning Orientation Field for OSM-Guided Autonomous Navigation
University of Macau（澳门大学）；Singapore Management University（新加坡管理大学）；University of Michigan（密歇根大学）；Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学）
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）
Mitigating Error Accumulation in Continuous Navigation via Memory-Augmented Kalman Filtering
Big Data Institute, Central South University, Changsha, China. Work done while working at CityUHK as a visiting scholar.；Department of Computer Science \& Institute of Digital Medicine, City University of Hong Kong, Hong Kong, China；School of Computer Science, Central South University, Changsha, China
EKF-Based Fusion of Wi-Fi/LiDAR/IMU for Indoor Localization and Navigation
School of Advanced Technology, Xi'an Jiaotong-Liverpool University（西安交通大学利物浦大学先进技术学院）；Department of Electrical Engineering and Electronics, University of Liverpool（利物浦大学电子工程系）；Zhejiang University of Technology（浙江工业大学）；School of Artificial Intelligence, Suzhou Vocational Institute of Industrial Technology（苏州工业职业技术学院人工智能学院）
RAGNav: A Retrieval-Augmented Topological Reasoning Framework for Multi-Goal Visual-Language Navigation
Southwestern University of Finance（西南财经大学）；Department of Informatics,Universitat Hamburg, Hamburg, Germany（汉堡大学信息学院）；University of Electronic Science（电子科技大学）
Safe and Efficient Social Navigation through Explainable Safety Regions Based on Topological Features
Department of Applied Mathematics I, University of Seville（应用数学系，塞维利亚大学）；CNR-IEIIT Genoa, Italy（意大利热那亚CNR-IEIIT）；SUPSI, IDSIA Lugano, Switzerland（瑞士卢加诺SUPSI，IDSIA）
The ability to autonomously navigate in unknown environments is important for mobile robots. The map is the core component to achieve this. Most map representations rely on drift-free state estimation and provide a global metric map to navigate. However, in large-scale real-world applications, it's hard to prohibit drifts and compose a globally consistent map quickly. In this paper, a novel representation named, HiTMap, is proposed to enhance the existing map representations. The central idea is to adopt a submap-based hierarchical topology rather than a global metric map so that only a local …
