scholarly journals IPS – a vision aided navigation system

2017 ◽  
Vol 6 (2) ◽  
Author(s):  
Anko Börner ◽  
Dirk Baumbach ◽  
Maximilian Buder ◽  
Andre Choinowski ◽  
Ines Ernst ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Degrees Of Freedom ◽  
Local Coordinate System ◽  
Human Head ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Six Degrees Of Freedom ◽  
Measurement Systems ◽  
External Reference ◽  
German Aerospace

AbstractEgo localization is an important prerequisite for several scientific, commercial, and statutory tasks. Only by knowing one’s own position, can guidance be provided, inspections be executed, and autonomous vehicles be operated. Localization becomes challenging if satellite-based navigation systems are not available, or data quality is not sufficient. To overcome this problem, a team of the German Aerospace Center (DLR) developed a multi-sensor system based on the human head and its navigation sensors – the eyes and the vestibular system. This system is called integrated positioning system (IPS) and contains a stereo camera and an inertial measurement unit for determining an ego pose in six degrees of freedom in a local coordinate system. IPS is able to operate in real time and can be applied for indoor and outdoor scenarios without any external reference or prior knowledge. In this paper, the system and its key hardware and software components are introduced. The main issues during the development of such complex multi-sensor measurement systems are identified and discussed, and the performance of this technology is demonstrated. The developer team started from scratch and transfers this technology into a commercial product right now. The paper finishes with an outlook.

Model Tests for Safe-Return-to-Port of a Damaged Ship in Waves

2015 ◽  
Author(s):  
Jeonghwa Seo ◽  
Cristobal Santiago Bravo ◽  
Shin Hyung Rhee
Keyword(s):  
Degrees Of Freedom ◽  
Feedback System ◽  
Model Tests ◽  
Measurement Unit ◽  
Test Model ◽  
Motion Response ◽  
Six Degrees Of Freedom ◽  
Heading Angle ◽  
Wave Conditions ◽  
Damaged Ship

A series of tests using a course-keeping model ship with an autopilot system were carried out in a towing tank for research on Safe-Return-to-Port (SRTP). The autopilot system controls the rudder angle and propeller revolution rate by a feedback system. The variation of the heading angle of the test model with different control parameters was investigated first, to ensure that the test model had sufficient course-keeping maneuverability in severe wave conditions. The wave conditions and propeller revolution rate were selected based on SRTP regulations. Tests were conducted in wave conditions corresponding to sea states 4 to 6. The six-degrees-of-freedom motion response of the test model was measured by a wireless inertial measurement unit and gyro sensors to achieve fully wireless model tests. The advance speed and motion response in various wave conditions were measured and analyzed to investigate the effects of flooding behavior in a damaged condition and of waves on the propulsion and maneuvering performance of the damaged ship model.

Trajectory Tracking of 3D Autonomous Vehicles Using Backstepping Control

2019 ◽  
Author(s):  
Karl Ludwig Fetzer ◽  
Sergey G. Nersesov ◽  
Hashem Ashrafiuon
Keyword(s):  
Autonomous Vehicles ◽  
Degrees Of Freedom ◽  
3D Model ◽  
Autonomous Vehicle ◽  
Backstepping Control ◽  
Reference Trajectory ◽  
Control Laws ◽  
Six Degrees Of Freedom ◽  
Error Dynamics ◽  
Dynamics Stability

Abstract In this paper, the authors derive backstepping control laws for tracking a time-based reference trajectory for a 3D model of an autonomous vehicle with two degrees of underactuation. Tracking all six degrees of freedom is made possible by a transformation that reduces the order of the error dynamics. Stability of the resulting error dynamics is proven and demonstrated in simulations.

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.

The Six Degrees of Freedom Motion of the Human Head, Spine, and Pelvis in a Frontal Impact

2013 ◽  
Vol 15 (3) ◽  
pp. 294-301 ◽  
Author(s):  
F. J. Lopez-Valdes ◽  
P. O. Riley ◽  
D. J. Lessley ◽  
K. B. Arbogast ◽  
T. Seacrist ◽  
...  
Keyword(s):  
Degrees Of Freedom ◽  
Human Head ◽  
Frontal Impact ◽  
Six Degrees Of Freedom

3D HEAD POSE NORMALIZATION WITH FACE GEOMETRY ANALYSIS, GENETIC ALGORITHMS AND PCA

2009 ◽  
Vol 18 (08) ◽  
pp. 1425-1439 ◽  
Author(s):  
VITOANTONIO BEVILACQUA ◽  
FRANCESCO ANDRIANI ◽  
GIUSEPPE MASTRONARDI
Keyword(s):  
Genetic Algorithms ◽  
Mirror Symmetry ◽  
Degrees Of Freedom ◽  
Human Head ◽  
Head Model ◽  
Geometrical Approach ◽  
Six Degrees Of Freedom ◽  
Pose Normalization ◽  
Automatic Alignment ◽  
Geometry Analysis

In this paper, a software toolchain is presented for the fully automatic alignment of a 3D human face model. Beginning from a point cloud of a human head (previously segmented from its background), pose normalization is obtained using an innovative and purely geometrical approach. In order to solve the six degrees of freedom raised by this problem, we first exploit the human face's natural mirror symmetry; secondly, we analyze the frontal profile shape; and finally, we align the model's bounding box according to the position of the tip of the nose. The whole procedure is considered as a two-fold, multivariable optimization problem which is addressed by the use of multi-level, genetic algorithms and a greedy search stage, with the latter being compared against standard PCA. Experiments were conducted utilizing a GavabDB database and took into account proper preprocessing stages for noise filtering and head model reconstruction. Outcome results reveal strong validity in this approach, however, at the price of high computational complexity.

Development of a Special Inertial Measurement Unit for UAV Applications

2012 ◽  
Vol 135 (1) ◽  
Author(s):  
Khaled S. Hatamleh ◽  
Ou Ma ◽  
Angel Flores-Abad ◽  
Pu Xie
Keyword(s):  
Degrees Of Freedom ◽  
Measurement Unit ◽  
Dynamics Modeling ◽  
Dynamics Model ◽  
Inertial Measurement ◽  
Six Degrees Of Freedom ◽  
On Line ◽  
Measurement Units ◽  
Sensor Models ◽  
And Control

Dynamics modeling is becoming more and more important in the development and control of unmanned aerial vehicles (UAV). An accurate model of a vehicle requires good knowledge of the dynamics properties and motion states, which are usually estimated with the help of integrated inertial measurement units (IMUs). This work develops a special six degrees of freedom IMU, which has the capability of measuring the angular accelerations. This paper introduces the design of the new IMU along with its sensor models and calibration procedures. The work introduces two experimental methods to verify the calibrated IMU readings. The IMU was designed to support an on-line methodology to estimate the parameters of UAV’s dynamics model that is currently being developed by the authors.

scholarly journals CFNet: LiDAR-Camera Registration Using Calibration Flow Network

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8112
Author(s):  
Xudong Lv ◽  
Shuo Wang ◽  
Dong Ye
Keyword(s):  
Autonomous Vehicles ◽  
Degrees Of Freedom ◽  
Image Data ◽  
Ground Truth ◽  
Geometric Information ◽  
Six Degrees Of Freedom ◽  
Calibration Methods ◽  
Extrinsic Parameters ◽  
Calibration Algorithm ◽  
Complex Optimization

As an essential procedure of data fusion, LiDAR-camera calibration is critical for autonomous vehicles and robot navigation. Most calibration methods require laborious manual work, complicated environmental settings, and specific calibration targets. The targetless methods are based on some complex optimization workflow, which is time-consuming and requires prior information. Convolutional neural networks (CNNs) can regress the six degrees of freedom (6-DOF) extrinsic parameters from raw LiDAR and image data. However, these CNN-based methods just learn the representations of the projected LiDAR and image and ignore the correspondences at different locations. The performances of these CNN-based methods are unsatisfactory and worse than those of non-CNN methods. In this paper, we propose a novel CNN-based LiDAR-camera extrinsic calibration algorithm named CFNet. We first decided that a correlation layer should be used to provide matching capabilities explicitly. Then, we innovatively defined calibration flow to illustrate the deviation of the initial projection from the ground truth. Instead of directly predicting the extrinsic parameters, we utilize CFNet to predict the calibration flow. The efficient Perspective-n-Point (EPnP) algorithm within the RANdom SAmple Consensus (RANSAC) scheme is applied to estimate the extrinsic parameters with 2D–3D correspondences constructed by the calibration flow. Due to its consideration of the geometric information, our proposed method performed better than the state-of-the-art CNN-based methods on the KITTI datasets. Furthermore, we also tested the flexibility of our approach on the KITTI360 datasets.

Control of processing at multi-tool two-support setup

2020 ◽  
pp. 67-73
Author(s):  
N.D. YUsubov ◽  
G.M. Abbasova
Keyword(s):  
Degrees Of Freedom ◽  
Factor Model ◽  
Angular Displacement ◽  
Factor Models ◽  
Technological System ◽  
Displacement Control ◽  
Model Accuracy ◽  
Six Degrees Of Freedom ◽  
Angular Displacements ◽  
And Control

The accuracy of two-tool machining on automatic lathes is analyzed. Full-factor models of distortions and scattering fields of the performed dimensions, taking into account the flexibility of the technological system on six degrees of freedom, i. e. angular displacements in the technological system, were used in the research. Possibilities of design and control of two-tool adjustment are considered. Keywords turning processing, cutting mode, two-tool setup, full-factor model, accuracy, angular displacement, control, calculation [email protected]

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