In this paper, we introduce a novel method to capture visual trajectories for navigating an indoor robot in dynamic settings using streaming image data. First, an image processing pipeline is proposed to accurately segment trajectories from noisy backgrounds. Next, the captured trajectories are used to design, train, and compare two neural network architectures for predicting acceleration and steering commands for a line following robot over a continuous space in real time. Lastly, experimental results demonstrate the performance of the neural networks versus human teleoperation of the robot and the viability of the system in environments with occlusions and/or low-light conditions.
SplaTraj: Camera Trajectory Generation with Semantic Gaussian Splatting
Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）；Joint Department of Biomedical Engineering, University of North Carolina-Chapel Hill（北卡罗来纳大学教堂山分校生物医学工程联合系）
We propose a new method to estimate the 6-dof trajectory of a flying object such as a quadrotor UAV within a 3D airspace monitored using multiple fixed ground cameras. It is based on a new structure from motion formulation for the 3D reconstruction of a single moving point with known motion dynamics. Our main contribution is a new bundle adjustment procedure which in addition to optimizing the camera poses, regularizes the point trajectory using a prior based on motion dynamics (or specifically flight dynamics). Furthermore, we can infer the underlying control input sent to the UAV's autopilot…
A Master-to-Simulation Interface with Demonstration-Trajectory Guidance for Vascular Intervention
Southern University of Science and Technology（南方科技大学）；Beijing Institute of Technology（北京理工大学）；Shenzhen Institute of Advanced Biomedical Robot Company Limited（深圳先进生物机器人有限公司）；Aerospace Center Hospital（航天中心医院）
Prediction skills can be crucial for the success of tasks where robots have limited time to act or joints actuation power. In such a scenario, a vision system with a fixed, possibly too low, sampling rate could lead to the loss of informative points, slowing down prediction convergence and reducing the accuracy. In this paper, we propose to exploit the low latency, motion-driven sampling, and data compression properties of event cameras to overcome these issues. As a use-case, we use a Panda robotic arm to intercept a ball bouncing on a table. To predict the interception point, we adopt a Stat…
Renault S.A.S（雷诺股份有限公司）；Sorbonne universités, Université de technologie de Compiègne, CNRS, Heudiasyc（索邦大学，贡比涅技术大学，法国国家科学研究中心，Heudiasyc实验室）；Monash University（莫纳什大学）
Camera-based end-to-end driving neural networks bring the promise of a low-cost system that maps camera images to driving control commands. These networks are appealing because they replace laborious hand engineered building blocks but their black-box nature makes them difficult to delve in case of failure. Recent works have shown the importance of using an explicit intermediate representation that has the benefits of increasing both the interpretability and the accuracy of networks' decisions. Nonetheless, these camera-based networks reason in camera view where scale is not homogeneous and he…
Automated Parking is becoming a standard feature in modern vehicles. Existing parking systems build a local map to be able to plan for maneuvering towards a detected slot. Next generation parking systems have an use case where they build a persistent map of the environment where the car is frequently parked, say for example, home parking or office parking. The pre-built map helps in re-localizing the vehicle better when its trying to park the next time. This is achieved by augmenting the parking system with a Visual SLAM pipeline and the feature is called trained trajectory parking in the auto…
Intelligent Systems & Control (ISaC) Lab, Department of Aerospace Engineering, Indian Institute of Technology Bombay（智能系统与控制实验室，航空航天工程系，印度理工学院孟买分校）；Guidance, Autonomy, Learning, and Control for Intelligent Systems (GALACxIS) Lab, Department of Aerospace Engineering and Engineering Mechanics, University of Cincinnati（智能系统引导、自主性、学习与控制（GALACxIS）实验室，航空航天工程与工程力学系，辛辛那提大学）
Expert Knowledge-driven Reinforcement Learning for Autonomous Racing via Trajectory Guidance and Dynamics Constraints
College of Automotive and Energy Engineering, Tongji University（同济大学汽车与能源工程学院）；School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航空航天工程学院）
A novel control & software architecture using ROS C++ is introduced for object interception by a UAV with a mounted depth camera and no external aid. Existing work in trajectory prediction focused on the use of off-board tools like motion capture rooms to intercept thrown objects. The present study designs the UAV architecture to be completely on-board capable of object interception with the use of a depth camera and point cloud processing. The architecture uses an iterative trajectory prediction algorithm for non-propelled objects like a ping-pong ball. A variety of path planning approaches t…
Crane Lowering Guidance Using a Attachable Camera Module for Driver Vision Support
Department of Interdisciplinary Robot Engineering Systems（交叉学科机器人工程系）；Department of Smart Construction Engineering（智能建造工程系）；Department of Robot Engineering（机器人工程系）；School of Smart Convergence Engineering（智能融合工程学院）
University of Oslo（奥斯陆大学）；Oslo University Hospital（奥斯陆大学医院）
With advancing technologies, robotic manipulators and visual environment sensors are becoming cheaper and more widespread. However, robot control can be still a limiting factor for better adaptation of these technologies. Robotic manipulators are performing very well in structured workspaces, but do not adapt well to unexpected changes, like people entering the workspace. We present a method combining 3D Camera based workspace mapping, and a predictive and reflexive robot manipulator trajectory estimation to allow more efficient and safer operation in dynamic workspaces. In experiments on a re…
Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）
We propose TRADE for robust tracking and 3D localization of a moving target in cluttered environments, from UAVs equipped with a single camera. Ultimately TRADE enables 3d-aware target following. Tracking-by-detection approaches are vulnerable to target switching, especially between similar objects. Thus, TRADE predicts and incorporates the target 3D trajectory to select the right target from the tracker's response map. Unlike static environments, depth estimation of a moving target from a single camera is a ill-posed problem. Therefore we propose a novel 3D localization method for ground targ…
Depth estimation is an important capability for autonomous vehicles to understand and reconstruct 3D environments as well as avoid obstacles during the execution. Accurate depth sensors such as LiDARs are often heavy, expensive and can only provide sparse depth while lighter depth sensors such as stereo cameras are noiser in comparison. We propose an end-to-end learning algorithm that is capable of using sparse, noisy input depth for refinement and depth completion. Our model also produces the camera pose as a byproduct, making it a great solution for autonomous systems. We evaluate our approa…
A Hybrid Sampling-Based Trajectory Planner with Game-Theoretic Guidance for Autonomous Racing
Professorship of Autonomous Vehicle Systems, TUM School of Engineering and Design, Technical University of Munich（慕尼黑工业大学工程与设计学院自动驾驶车辆系统教授组）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）
An Efficient LiDAR-Camera Fusion Network for Multi-Class 3D Dynamic Object Detection and Trajectory Prediction
Institute of Medical Robotics and Department of Automation, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education（医学机器人研究所和自动化系，上海交通大学，系统控制与信息处理重点实验室，教育部）
Faculty of Electrical Engineering and Computing, University of Zagreb（萨格勒布大学电气工程与计算学院）
This paper presents our approach to intercepting a faster intruder UAV, inspired by the MBZIRC 2020 Challenge 1. By utilizing a priori knowledge of the shape of the intruder's trajectory, we can calculate an interception point. Target tracking is based on image processing by a YOLOv3 Tiny convolutional neural network, combined with depth calculation using a gimbal-mounted ZED Mini stereo camera. We use RGB and depth data from the camera, devising a noise-reducing histogram-filter to extract the target's 3D position. Obtained 3D measurements of target's position are used to calculate the positi…
Ramp merging is a typical application of cooperative intelligent transportation system (C-ITS). Vehicle trajectories perceived by roadside sensors are importation complement to the limited visual field of on-board perception. Vehicle tracking and trajectory denoising algorithm is proposed in this paper to take full advantage of roadside cameras for vehicle trajectory and speed profile estimation. Dynamic speed guidance algorithm is proposed to help on-ramp vehicles to merge into mainline smoothly, even in non-cooperative environment where mainline vehicles are not expected to slow down to acco…
Institute of Industrial Science, The University of Tokyo（东京大学生产技术研究所）
For the optimum design of a driver-automation shared control system, an understanding of driver behavior based on measurements and modeling is crucial early in the development process. This paper presents a driver model through a weighting process of visual guidance from the road ahead and haptic guidance from a steering system for a lane-following task. The proposed weighting process describes the interaction of a driver with the haptic guidance steering and the driver reliance on it. A driving simulator experiment is conducted to identify the model parameters for driving manually and with ha…
Instituto de Investigación en Ingeniería de Aragón（阿拉贡工程研究所）；Universidad de Zaragoza（萨拉戈萨大学）；Stanford University（斯坦福大学）
We present CineMPC, an algorithm to autonomously control a UAV-borne video camera in a nonlinear Model Predicted Control (MPC) loop. CineMPC controls both the position and orientation of the camera -- the camera extrinsics -- as well as the lens focal length, focal distance, and aperture -- the camera intrinsics. While some existing solutions autonomously control the position and orientation of the camera, no existing solutions also control the intrinsic parameters, which are essential tools for rich cinematographic expression. The intrinsic parameters control the parts of the scene that are f…
