This paper describes a stereo image-based visual servoing system for trajectory tracking by a non-holonomic robot without externally derived pose information nor a known visual map of the environment. It is called trajectory servoing. The critical component is a feature-based, indirect Simultaneous Localization And Mapping (SLAM) method to provide a pool of available features with estimated depth, so that they may be propagated forward in time to generate image feature trajectories for visual servoing. Short and long distance experiments show the benefits of trajectory servoing for navigating unknown areas without absolute positioning. Empirically, trajectory servoing has better trajectory tracking performance than pose-based feedback when both rely on the same underlying SLAM system.
Despite the existence of different error metrics for trajectory evaluation in SLAM, their theoretical justifications and connections are rarely studied, and few methods handle temporal association properly. In this work, we propose to formulate the trajectory evaluation problem in a probabilistic, continuous-time framework. By modeling the groundtruth as random variables, the concepts of absolute and relative error are generalized to be likelihood. Moreover, the groundtruth is represented as a piecewise Gaussian Process in continuous-time. Within this framework, we are able to establish theore…
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 …
We present an unsupervised deep neural network approach to the fusion of RGB-D imagery with inertial measurements for absolute trajectory estimation. Our network, dubbed the Visual-Inertial-Odometry Learner (VIOLearner), learns to perform visual-inertial odometry (VIO) without inertial measurement unit (IMU) intrinsic parameters (corresponding to gyroscope and accelerometer bias or white noise) or the extrinsic calibration between an IMU and camera. The network learns to integrate IMU measurements and generate hypothesis trajectories which are then corrected online according to the Jacobians o…
GNSS and LiDAR odometry are complementary as they provide absolute and relative positioning, respectively. Their integration in a loosely-coupled manner is straightforward but is challenged in urban canyons due to the GNSS signal reflections. Recent proposed 3D LiDAR-aided (3DLA) GNSS methods employ the point cloud map to identify the non-line-of-sight (NLOS) reception of GNSS signals. This facilitates the GNSS receiver to obtain improved urban positioning but not achieve a sub-meter level. GNSS real-time kinematics (RTK) uses carrier phase measurements to obtain decimeter-level positioning. I…
Naver Labs Europe；Universitat Jaume I de Castello（海梅一世大学）
We address the indoor localization problem, where the goal is to predict user's trajectory from the data collected by their smartphone, using inertial sensors such as accelerometer, gyroscope and magnetometer, as well as other environment and network sensors such as barometer and WiFi. Our system implements a deep learning based pedestrian dead reckoning (deep PDR) model that provides a high-rate estimation of the relative position of the user. Using Kalman Filter, we correct the PDR's drift using WiFi that provides a prediction of the user's absolute position each time a WiFi scan is received…
Dept. of Electrical Engineering and Computer Science, KTH Royal Institute of Technology（电气工程与计算机科学系，皇家理工学院）；Dept. of Electrical Engineering, Linköping University（电气工程系，林波大学）；Dept.of Electrical Engineering and Computer Science, KTH Royal Institute of Technology（电气工程与计算机科学系，皇家理工学院）；Division of Underwater Technology, Swedish Defence Research Agency (FOI)（水下技术 division，瑞典国防研究机构（FOI））
Joint UAV-UGV Positioning and Trajectory Planning via Meta A3C for Reliable Emergency Communications
Department of Computer Science, Texas A&M University-Corpus Christi（德克萨斯农工大学科珀斯克里斯蒂分校计算机科学系）；School of Computer Science, University of Oklahoma（俄克拉荷马大学计算机科学学院）
Manipulation and grasping with unmanned aerial vehicles (UAVs) currently require accurate positioning and are often executed at reduced speed to ensure successful grasps. This is due to the fact that typical UAVs can only accommodate rigid manipulators with few degrees of freedom, which limits their capability to compensate for disturbances caused by the vehicle positioning errors. Moreover, UAVs have to minimize external contact forces in order to maintain stability. Biological systems, on the other hand, exploit softness to overcome similar limitations, and leverage compliance to enable aggr…
Advanced Research Lab, NavInfo Europe（NavInfo欧洲先进研究实验室）
Autonomous vehicles and driver assistance systems utilize maps of 3D semantic landmarks for improved decision making. However, scaling the mapping process as well as regularly updating such maps come with a huge cost. Crowdsourced mapping of these landmarks such as traffic sign positions provides an appealing alternative. The state-of-the-art approaches to crowdsourced mapping use ground truth camera parameters, which may not always be known or may change over time. In this work, we demonstrate an approach to computing 3D traffic sign positions without knowing the camera focal lengths, princip…
The existing methods for trajectory prediction are difficult to describe trajectory of moving objects in complex and uncertain environment accurately. In order to solve this problem, this paper proposes an adaptive trajectory prediction method for moving objects based on variation Gaussian mixture model (VGMM) in dynamic environment (ESATP). Firstly, based on the traditional mixture Gaussian model, we use the approximate variational Bayesian inference method to process the mixture Gaussian distribution in model training procedure. Secondly, variational Bayesian expectation maximization iterati…
Absolute positioning is an essential factor for the arrival of autonomous driving. Global Navigation Satellites System (GNSS) receiver provides absolute localization for it. GNSS solution can provide satisfactory positioning in open or sub-urban areas, however, its performance suffered in super-urbanized area due to the phenomenon which are well-known as multipath effects and NLOS receptions. The effects dominate GNSS positioning performance in the area. The recent proposed 3D map aided (3DMA) GNSS can mitigate most of the multipath effects and NLOS receptions caused by buildings based on 3D c…
An Aerial Manipulator for Perception-Driven Flower Targeting Toward Contactless Pollination in Vertical Farming
Department of Computer Science University College London London United Kingdom（计算机科学系伦敦大学学院伦敦英国）；Department of Artificial Intelligence University of Malaya Kuala Lumpur Malaysia（人工智能系马来大学吉隆坡马来西亚）；University College London（伦敦大学学院）；University of Malaya（马来大学）
Out-of-Sight Embodied Agents: Multimodal Tracking, Sensor Fusion, and Trajectory Forecasting
Department of Electrical and Computer Engineering, Northeastern University（东北大学电气与计算机工程系）；Khoury College of Computer Sciences, Northeastern University（东北大学库里计算机科学学院）
Towards Multi-Object Nonprehensile Transportation via Shared Teleoperation: A Framework Based on Virtual Object Model Predictive Control
State Key Laboratory of Robotics and System, Harbin Institute of Technology（哈尔滨工业大学机器人技术与系统国家重点实验室）；Advanced Manufacturing Laboratory, Huawei Technologies（华为技术有限公司先进制造实验室）
RCM-Constrained Manipulator Trajectory Tracking Using Differential Kinematics Control
Center for Artificial Intelligence and Robotics (CAIR), New York University Abu Dhabi (NYUAD)（纽约大学阿布扎比分校人工智能与机器人中心（CAIR））；Electrical Engineering, NYUAD（纽约大学阿布扎比分校电气工程系）
