We consider the problem of carrier-phase differential GPS positioning for an land vehicle navigation system (LVNS), tightly coupled with an inertial measurement unit (IMU) and a speedometer. The primary focus is to apply Bayesian network to an IMU-aided GPS positioning system based on carrier-phase differential GPS. We describe the implementation details of the positioning system that integrates GPS measurements (i.e., pseudo-range, carrier-phase and doppler), IMU measurements, and speedometer measurements. We derive the linearized state process equation and the measurement equation for GPS and speedometer. To account for constraints of land vehicle, we add two more pseudo measurements to ensure the perpendicular velocities close to zero.
Data-driven based method for navigation and positioning has absorbed attention in recent years and it outperforms all its competitor methods in terms of accuracy and efficiency. This paper introduces a new architecture called IMUNet which is accurate and efficient for position estimation on edge device implementation receiving a sequence of raw IMU measurements. The architecture has been compared with one dimension version of the state-of-the-art CNN networks that have been introduced recently for edge device implementation in terms of accuracy and efficiency. Moreover, a new method for collec…
South China University of Technology（华南理工大学）；City University of Hong Kong（香港城市大学）
In this paper, we present a tightly coupled optimization-based GPS-Visual-Inertial odometry system to solve the trajectory drift of the visual-inertial odometry especially over long-term runs. Visual reprojection residuals, IMU residuals, and GPS measurement residuals are jointly minimized within a local bundle adjustment, in which we apply GPS measurements and IMU preintegration used for the IMU residuals to formulate a novel GPS residual. To improve the efficiency and robustness of the system, we propose a fast reference frames initialization method and an online calibration method for GPS-I…
High-Precision Climbing Robot Localization Using Planar Array UWB/GPS/IMU/Barometer Integration
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Department of Data Science and AI, Monash University（数据科学与人工智能系，墨尔本大学）；Department of Data Science and Artificial Intelligence, Faculty of Infomation Technology, Monash University（数据科学与人工智能系，信息科技学院，墨尔本大学）
In the presented scenario, an autonomous surface vehicle (ASV) equipped with a laser scanner navigates on a inland pathway surrounded and crossed by man-made structures such as bridges and locks. {GPS} receiver present on board experiences signal loss and multipath reflections in situation when the view of the sky is obscured by a bridge or tall buildings. In both cases, a potentially dangerous situation is provoked as the robot has no or inaccurate positioning data. A sensor data processing scheme is proposed where these gaps are smoothly filled in by positioning data generated from scan matc…
University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）
The combination of ultrawideband (UWB) radios and inertial measurement units (IMU) can provide accurate positioning in environments where the Global Positioning System (GPS) service is either unavailable or has unsatisfactory performance. The two sensors, IMU and UWB radio, are often not co-located on a moving system. The UWB radio is typically located at the extremities of the system to ensure reliable communication, whereas the IMUs are located closer to its center of gravity. Furthermore, without hardware or software synchronization, data from heterogeneous sensors can arrive at different t…
Shenzhen Unity-Drive Innovation Technology Co. Ltd.（深圳一清创新科技有限公司）；Hong Kong University of Science and Technology（香港科技大学）；Hangzhou ATG Intelligent Technology Co. Ltd.（杭州ATG智能科技有限公司）
Using different sensors in an autonomous vehicle (AV) can provide multiple constraints to optimize AV location estimation. In this paper, we present a low-cost GPS-assisted LiDAR state estimation system for AVs. Firstly, we utilize LiDAR to obtain highly precise 3D geometry data. Next, we use an inertial measurement unit (IMU) to correct point cloud misalignment caused by incorrect place recognition. The estimated LiDAR odometry and IMU measurement are then jointly optimized. We use a lost-cost GPS instead of a real-time kinematic (RTK) module to refine the estimated LiDAR-inertial odometry. O…
A brain-inspired information fusion method for enhancing robot GPS outages navigation
Guangdong Institute of Intelligence Science and Technology（广东智能科学与技术研究院）；School of Electronic Engineering and Automation, Guilin University of Electronic Technology（桂林电子科技大学电子工程与自动化学院）；Innovation and Research and Development Department, BoardWare Information System Company Ltd（BoardWare信息系统公司创新与研发部）
University of Toronto Institute for Aerospace Studies（多伦多大学航空航天研究所）
Mobile robots rely on odometry to navigate through areas where localization fails. Visual odometry (VO) is a common solution for obtaining robust and consistent relative motion estimates of the vehicle frame. Contrarily, Global Positioning System (GPS) measurements are typically used for absolute positioning and localization. However, when the constraint on absolute accuracy is relaxed, time-differenced carrier phase (TDCP) measurements can be used to find accurate relative position estimates with one single-frequency GPS receiver. This suggests practitioners may want to consider GPS odometry …
The self-localization capability is a crucial component for Unmanned Ground Vehicles (UGV) in farming applications. Approaches based solely on visual cues or on low-cost GPS are easily prone to fail in such scenarios. In this paper, we present a robust and accurate 3D global pose estimation framework, designed to take full advantage of heterogeneous sensory data. By modeling the pose estimation problem as a pose graph optimization, our approach simultaneously mitigates the cumulative drift introduced by motion estimation systems (wheel odometry, visual odometry, ...), and the noise introduced …
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）；School of Automation, SouthEast University（东南大学自动化学院）
Mobile robot positioning, mapping, and navigation systems generally employ an inertial measurement unit (IMU) to obtain the acceleration and angular velocity of the robot. However, errors in the internal and external parameters of an IMU arising from defective calibration directly affect the accuracy of robot positioning and pose estimation. While this issue has been addressed by the mature internal reference calibration methods available for IMUs, external reference calibration methods between the IMU and the chassis of a mobile robot are lacking. This study addresses this issue by proposing …
Adaptive Factor Graph-Based Tightly Coupled GNSS/IMU Fusion for Robust Positionin
School of Technology and Innovations, University of Vaasa（瓦萨大学技术与创新学院）；Information Technology, Åbo Akademi University（阿博阿卡迪米大学信息技术系）；Faculty of Information Technology and Communication Sciences, Tampere University（塔尔库大学信息科技与通信科学学院）；Finnish Geospatial Research Institute（芬兰地理空间研究 institute）
Nanyang Technological University（南洋理工大学）；Institute for Infocomm Research, A-STAR（新加坡科技研究局资讯通信研究院）
Employing an inertial measurement unit (IMU) as an additional sensor can dramatically improve both reliability and accuracy of visual/Lidar odometry (VO/LO). Different IMU integration models are introduced using different assumptions on the linear acceleration from the IMU. In this paper, a novel IMU integration model is proposed by using switched linear systems. The proposed approach assumes that both the linear acceleration and the angular velocity in the body frame are constant between two consecutive IMU measurements. This is more realistic in real world situation compared to existing appr…
University of Bristol（布里斯托大学）；Perceptual Robotics（感知机器人公司）
We present a novel method of integrating image-based measurements into a drone navigation system for the automated inspection of wind turbines. We take a model-based tracking approach, where a 3D skeleton representation of the turbine is matched to the image data. Matching is based on comparing the projection of the representation to that inferred from images using a convolutional neural network. This enables us to find image correspondences using a generic turbine model that can be applied to a wide range of turbine shapes and sizes. To estimate 3D pose of the drone, we fuse the network outpu…
This paper addresses the issues of unmanned aerial vehicle (UAV) indoor navigation, specifically in areas where GPS and magnetometer sensor measurements are unavailable or unreliable. The proposed solution is to use an error state extended Kalman filter (ES -EKF) in the context of multi-sensor fusion. Its implementation is adapted to fuse measurements from multiple sensor sources and the state model is extended to account for sensor drift and possible calibration inaccuracies. Experimental validation is performed by fusing IMU data obtained from the PixHawk 2.1 flight controller with pose meas…
Nonlinear Deterministic Observer for Inertial Navigation using Ultra-wideband and IMU Sensor Fusion
Carleton University（卡尔顿大学）；The Hong Kong Polytechnic University（香港理工大学）；Georgia Institute of Technology（佐治亚理工学院）；Bowling Green State University（鲍灵格林州立大学）
Loosely Coupled Factor Graph Optimization for Pseudolite-Augmented Navigation
Federal Ministry for Economic Affairs and Energy, Germany（德国经济事务和能源部长办公厅）；German Federal Ministry of Research, Technology, and Space (BMFTR)（德国联邦研究、技术和空间部长办公厅）；Robotics Institute Germany (RIG)（德国机器人研究所）
Real-time tightly coupled GNSS and IMU integration via Factor Graph Optimization
University Politehnica of Bucharest（巴特亚大学）；Three Tensors S.R.L.（Three Tensors公司）；University of Bucharest（布加勒斯特大学）；Navigation Systems Definition Section (TEC-SEN), ESA（欧洲航天局导航系统定义部门）；Romanian InSpace Engineering S.R.L. (RISE)（罗马尼亚InSpace工程公司）
Robust, high-precision global localization is fundamental to a wide range of outdoor robotics applications. Conventional fusion methods use low-accuracy pseudorange based GNSS measurements ($>>5m$ errors) and can only yield a coarse registration to the global earth-centered-earth-fixed (ECEF) frame. In this paper, we leverage high-precision GNSS carrier-phase positioning and aid it with local visual-inertial odometry (VIO) tracking using an extended Kalman filter (EKF) framework that better resolves the integer ambiguity concerned with GNSS carrier-phase. %to achieve centimeter-level accuracy …
Control and State Estimation of Vehicle-Mounted Aerial Systems in GPS-Denied, Non-Inertial Environments
Robotics, Intelligent Systems, and Control Lab, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia（机器人、智能系统与控制实验室，国王阿卜杜勒-阿齐兹大学科学与技术学院（KAUST），沙特阿拉伯图瓦尔）
Overcoming the Intrinsic Performance Limitations of MEMS IMU via Diffusion-Based Generative Learning
School of Geodesy and Geomatics, Wuhan University（武汉大学测绘学院）；Hubei Luojia Laboratory, Wuhan University（湖北珞珈实验室）；Chinese Antarctic Center of Surveying and Mapping, Wuhan University（中国极地测绘南极科考中心）
