This paper presents a state-of-the-art approach in object detection for being applied in future SLAM problems. Although, many SLAM methods are proposed to create suitable autonomy for mobile robots namely ground vehicles, they still face overconfidence and large computations during entrance to immense spaces with many landmarks. In particular, they suffer from impractical applications via sole reliance on the limited sensors like camera. Proposed method claims that unmanned ground vehicles without having huge amount of database for object definition and highly advance prediction parameters can deal with incoming objects during straight motion of camera in real-time. Line-Circle (LC) filter tries to apply detection, tracking and learning to each defined experts to obtain more information for judging scene without over-calculation. In this filter, circle expert let us summarize edges in groups. The Interactive feedback learning between each expert creates minimal error that fights against overwhelming landmark signs in crowded scenes without mapping. Our experts basically are dependent on trust factors' covariance with geometric definitions to ignore, emerge and compare detected landmarks. The experiment for validating the model is taken place utilizing a camera beside an IMU sensor for location estimation.
Kyushu University（九州大学）；University of York（约克大学）；University of Tabriz（大不里士大学）
This paper presents a state-of-the-art filter that reduces the complexity in object detection, tracking and mapping applications. Existing edge detection and tracking methods are proposed to create suitable autonomy for mobile robots, however, many of them face overconfidence and large computations at the entrance to scenarios with an immense number of landmarks. The method in this work, the Line-Circle-Square (LCS) filter, claims that mobile robots without a large database for object recognition and highly advanced prediction methods can deal with incoming objects that the camera captures in …
Complex autonomous driving, such as drifting, requires high-precision and high-frequency pose information to ensure accuracy and safety, which is notably difficult when using only onboard sensors. In this paper, we propose a drift controller with two feedback control loops: sideslip controller that stabilizes the sideslip angle by tuning the front wheel steering angle, and circle controller that maintains a stable trajectory radius and circle center by controlling the wheel rotational speed. We use an extended Kalman filter to estimate the state. A robustified KASA algorithm is further propose…
Reliable knowledge of road boundaries is critical for autonomous vehicle navigation. We propose a robust curb detection and filtering technique based on the fusion of camera semantics and dense lidar point clouds. The lidar point clouds are collected by fusing multiple lidars for robust feature detection. The camera semantics are based on a modified EfficientNet architecture which is trained with labeled data collected from onboard fisheye cameras. The point clouds are associated with the closest curb segment with $L_2$-norm analysis after projecting into the image space with the fisheye model…
Localization Through Particle Filter Powered Neural Network Estimated Monocular Camera Poses
University of Michigan（密歇根大学）；Northeastern University, Shenyang（东北大学（沈阳））；University of Pennsylvania（宾夕法尼亚大学）；Columbia University（哥伦比亚大学）；Carnegie Mellon University（卡内基梅隆大学）
University of Michigan（密歇根大学）；University of California, Berkeley（加州大学伯克利分校）
This paper proposes a state estimator for legged robots operating in slippery environments. An Invariant Extended Kalman Filter (InEKF) is implemented to fuse inertial and velocity measurements from a tracking camera and leg kinematic constraints. {\color{black}The misalignment between the camera and the robot-frame is also modeled thus enabling auto-calibration of camera pose.} The leg kinematics based velocity measurement is formulated as a right-invariant observation. Nonlinear observability analysis shows that other than the rotation around the gravity vector and the absolute position, all…
Monitoring wildlife through camera traps produces a massive amount of images, whose a significant portion does not contain animals, being later discarded. Embedding deep learning models to identify animals and filter these images directly in those devices brings advantages such as savings in the storage and transmission of data, usually resource-constrained in this type of equipment. In this work, we present a comparative study on animal recognition models to analyze the trade-off between precision and inference latency on edge devices. To accomplish this objective, we investigate classifiers …
University of Washington（华盛顿大学）；University of Texas at Dallas（德克萨斯大学达拉斯分校）
In this paper, we propose a multi-object detection and tracking method using depth cameras. Depth maps are very noisy and obscure in object detection. We first propose a region-based method to suppress high magnitude noise which cannot be filtered using spatial filters. Second, the proposed method detect Region of Interests by temporal learning which are then tracked using weighted graph-based approach. We demonstrate the performance of the proposed method on standard depth camera datasets with and without object occlusions. Experimental results show that the proposed method is able to suppres…
This paper presents a novel method for pedestrian detection and tracking by fusing camera and LiDAR sensor data. To deal with the challenges associated with the autonomous driving scenarios, an integrated tracking and detection framework is proposed. The detection phase is performed by converting LiDAR streams to computationally tractable depth images, and then, a deep neural network is developed to identify pedestrian candidates both in RGB and depth images. To provide accurate information, the detection phase is further enhanced by fusing multi-modal sensor information using the Kalman filte…
Navigation using only one marker, which contains four artificial features, is a challenging task since camera pose estimation using only four coplanar points suffers from the rotational ambiguity problem in a real-world application. This paper presents a framework of vision-based navigation for a self-driving vehicle equipped with multiple cameras and a wheel odometer. A multiple camera setup is presented for the camera cluster which has 360-degree vision such that our framework solely requires one planar marker. A Kalman-Filter-based fusion method is introduced for the multiple-camera and whe…
The University of Tokyo（东京大学）；Osaka University（大阪大学）
This paper discusses video motion capture, namely, 3D reconstruction of human motion from multi-camera images. After the Part Confidence Maps are computed from each camera image, the proposed spatiotemporal filter is applied to deliver the human motion data with accuracy and smoothness for human motion analysis. The spatiotemporal filter uses the human skeleton and mixes temporal smoothing in two-time inverse kinematics computations. The experimental results show that the mean per joint position error was 26.1mm for regular motions and 38.8mm for inverted motions.
Event cameras are ideally suited to capture HDR visual information without blur but perform poorly on static or slowly changing scenes. Conversely, conventional image sensors measure absolute intensity of slowly changing scenes effectively but do poorly on high dynamic range or quickly changing scenes. In this paper, we present an event-based video reconstruction pipeline for High Dynamic Range (HDR) scenarios. The proposed algorithm includes a frame augmentation pre-processing step that deblurs and temporally interpolates frame data using events. The augmented frame and event data are then fu…
MonoVisual3DFilter: 3D tomatoes' localisation with monocular cameras using histogram filters
Faculty of Engineering, University of Porto（波尔图大学工程学院）；INESC TEC – Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência（INESC TEC – 系统与计算机工程、技术与科学研究所）；Khalifa University of Science, Technology, and Research（哈利法科技研究大学）
Active surgical robots lack acceptance in clinical practice, because they do not offer the flexibility and usability required for a versatile usage: the systems require a large installation space or a complicated registration step, where the preoperative plan is aligned to the patient and transformed to the base frame of the robot. In this paper, a navigation system for robotic osteotomies is designed, which uses the raw depth images from a camera mounted on the flange of a lightweight robot arm. Consequently, the system does not require any rigid attachment of the robot or fiducials to the bo…
Wuhan University（武汉大学）；Wuhan Institute of Quantum Technology（武汉量子技术研究院）；ETH Zürich（苏黎世联邦理工学院）；The Ohio State University（俄亥俄州立大学）
Camera-IMU (Inertial Measurement Unit) sensor fusion has been extensively studied in recent decades. Numerous observability analysis and fusion schemes for motion estimation with self-calibration have been presented. However, it has been uncertain whether both camera and IMU intrinsic parameters are observable under general motion. To answer this question, by using the Lie derivatives, we first prove that for a rolling shutter (RS) camera-IMU system, all intrinsic and extrinsic parameters, camera time offset, and readout time of the RS camera, are observable with an unknown landmark. To our kn…
Accurate Measurement of 3D and 2D Circular Centers With Application to LiDAR-Camera Extrinsic Calibration
Thrust of Robotics and Autonomous Systems, The Hong Kong University of Science and Technology (Guangzhou)（机器人与自主系统研究所，香港科技大学（广州））；Lower Airspace Economy Research Institute, International Digital Economy Academy, ShenZhen（空域经济研究所，国际数字经济学院）；Horizon-Continental Technology Corporation（Horizon-Continental技术公司）；State Key Laboratory of Rail Traffic Control and Safety and the Beijing Engineering Research Center of EMC and GNSS Technology for Rail Transportation, School of Electronic and Information Engineering, Beijing Jiaotong University（轨道交通控制与安全国家重点实验室，北京交通大学电子信息工程学院，电磁兼容与GNSS技术轨道交通工程研究中心）
DynamicPose: Real-time and Robust 6D Object Pose Tracking for Fast-Moving Cameras and Objects
School of Artificial Intelligence, Sun Yat-Sen University（中山大学人工智能学院）；School of Mechanical Engineering, Xi’an Jiaotong University（西安交通大学机械工程学院）；Department of Mechanical and Energy Engineering, Southern University of Science and Technology（南方科技大学机械与能源工程系）
Wuhan University（武汉大学）；ETH Zurich（苏黎世联邦理工学院）；The Ohio State University（俄亥俄州立大学）
Motion estimation by fusing data from at least a camera and an Inertial Measurement Unit (IMU) enables many applications in robotics. However, among the multitude of Visual Inertial Odometry (VIO) methods, few efficiently estimate device motion with consistent covariance, and calibrate sensor parameters online for handling data from consumer sensors. This paper addresses the gap with a Keyframe-based Structureless Filter (KSF). For efficiency, landmarks are not included in the filter's state vector. For robustness, KSF associates feature observations and manages state variables using the conce…
This paper addresses recursive markerless estimation of a robot's end-effector using visual observations from its cameras. The problem is formulated into the Bayesian framework and addressed using Sequential Monte Carlo (SMC) filtering. We use a 3D rendering engine and Computer Aided Design (CAD) schematics of the robot to virtually create images from the robot's camera viewpoints. These images are then used to extract information and estimate the pose of the end-effector. To this aim, we developed a particle filter for estimating the position and orientation of the robot's end-effector using …
In this paper, a self-calibration approach for eye-in-hand robots using SLAM is considered. The goal is to calibrate the positioning of a robotic arm, with a camera mounted on the end-effector automatically using a SLAM-based method like Extended Kalman Filter (EKF). Given the camera intrinsic parameters and a set of feature markers in a work-space, the camera extrinsic parameters are approximated. An EKF based measurement model is deployed to effectively localize the camera and compute the camera to end-effector transformation. The proposed approach is tested on a UR5 manipulator with a depth…
