scholarly journals An Ultrasonic Six Degrees-of-Freedom Pose Estimation Sensor

2017 ◽  
Vol 17 (1) ◽  
pp. 151-159 ◽  
Author(s):  
Dennis Laurijssen ◽  
Steven Truijen ◽  
Wim Saeys ◽  
Walter Daems ◽  
Jan Steckel
Keyword(s):  
Pose Estimation ◽  
Degrees Of Freedom ◽  
Six Degrees Of Freedom

scholarly journals Pose Estimation of Primitive-Shaped Objects from a Depth Image Using Superquadric Representation

2020 ◽  
Vol 10 (16) ◽  
pp. 5442
Author(s):  
Ryo Hachiuma ◽  
Hideo Saito
Keyword(s):  
Pose Estimation ◽  
Degrees Of Freedom ◽  
Shape Representation ◽  
Estimation Method ◽  
Depth Image ◽  
Six Degrees Of Freedom ◽  
Depth Images ◽  
Object Pose Estimation ◽  
Primitive Shape ◽  
Conventional Methods

This paper presents a method for estimating the six Degrees of Freedom (6DoF) pose of texture-less primitive-shaped objects from depth images. As the conventional methods for object pose estimation require rich texture or geometric features to the target objects, these methods are not suitable for texture-less and geometrically simple shaped objects. In order to estimate the pose of the primitive-shaped object, the parameters that represent primitive shapes are estimated. However, these methods explicitly limit the number of types of primitive shapes that can be estimated. We employ superquadrics as a primitive shape representation that can represent various types of primitive shapes with only a few parameters. In order to estimate the superquadric parameters of primitive-shaped objects, the point cloud of the object must be segmented from a depth image. It is known that the parameter estimation is sensitive to outliers, which are caused by the miss-segmentation of the depth image. Therefore, we propose a novel estimation method for superquadric parameters that are robust to outliers. In the experiment, we constructed a dataset in which the person grasps and moves the primitive-shaped objects. The experimental results show that our estimation method outperformed three conventional methods and the baseline method.

Can we Localize an AV from a Single Image? Deep-Geometric 6 DoF Localization in Topo-metric Maps

2021 ◽  
pp. 1-13
Author(s):  
Punarjay Chakravarty ◽  
Tom Roussel ◽  
Gaurav Pandey ◽  
Tinne Tuytelaars
Keyword(s):  
Pose Estimation ◽  
Autonomous Vehicles ◽  
Hybrid Algorithm ◽  
Degrees Of Freedom ◽  
Single Camera ◽  
Single Image ◽  
Six Degrees Of Freedom ◽  
Localization Algorithms ◽  
Mapping Process ◽  
Set Of Points

Abstract We describe a Deep-Geometric Localizer that is able to estimate the full six degrees-of-freedom (DoF) global pose of the camera from a single image in a previously mapped environment. Our map is a topo-metric one, with discrete topological nodes whose 6DOF poses are known. Each topo-node in our map also comprises of a set of points, whose 2D features and 3D locations are stored as part of the mapping process. For the mapping phase, we utilise a stereo camera and a regular stereo visual SLAM pipeline. During the localization phase, we take a single camera image, localize it to a topological node using Deep Learning, and use a geometric algorithm (PnP) on the matched 2D features (and their 3D positions in the topo map) to determine the full 6DOF globally consistent pose of the camera. Our method divorces the mapping and the localization algorithms and sensors (stereo and mono), and allows accurate 6DOF pose estimation in a previously mapped environment using a single camera. With results in simulated and real environments, our hybrid algorithm is particularly useful for autonomous vehicles (AVs) and shuttles that might repeatedly traverse the same route.

scholarly journals Automatic Calibration of a Six-Degrees-of-Freedom Pose Estimation System

2019 ◽  
Vol 19 (19) ◽  
pp. 8824-8831 ◽  
Author(s):  
Wouter Jansen ◽  
Dennis Laurijssen ◽  
Walter Daems ◽  
Jan Steckel
Keyword(s):  
Pose Estimation ◽  
Degrees Of Freedom ◽  
Automatic Calibration ◽  
Six Degrees Of Freedom ◽  
Estimation System

Vision Based 3D Tracking and Pose Estimation for Mixed Reality

2011 ◽  
pp. 1-22 ◽  
Author(s):  
Pascal Fua ◽  
Vincent Lepetit
Keyword(s):  
Pose Estimation ◽  
Degrees Of Freedom ◽  
Mixed Reality ◽  
Low Cost ◽  
3D Tracking ◽  
Robust Algorithms ◽  
Six Degrees Of Freedom ◽  
Non Invasive ◽  
Camera Position ◽  
Position And Orientation

Mixed Reality applications require accurate knowledge of the relative positions of the camera and the scene. When either of them moves, this means keeping track in real-time of all six degrees of freedom that define the camera position and orientation relative to the scene, or, equivalently, the 3D displacement of an object relative to the camera. Many technologies have been tried to achieve this goal. However, Computer Vision is the only one that has the potential to yield non-invasive, accurate and low-cost solutions to this problem, provided that one is willing to invest the effort required to develop sufficiently robust algorithms. In this chapter, we therefore discuss some of the most promising approaches, their strengths, and their weaknesses.

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