3D Reconstruction for Road Scene with Obstacle Detection Feedback

2018 ◽  
Vol 32 (12) ◽  
pp. 1855021 ◽  
Author(s):  
Huanbing Gao ◽  
Lei Liu ◽  
Ya Tian ◽  
Shouyin Lu
Keyword(s):  
3D Reconstruction ◽  
Laser Scanning ◽  
Autonomous Driving ◽  
Calibration Method ◽  
Obstacle Detection ◽  
Reconstruction Method ◽  
Navigation Systems ◽  
Driver Assistance ◽  
The Road ◽  
Angle Measuring

This paper presented 3D reconstruction method for road scene with the help of obstacle detection. 3D reconstruction for road scene can be used in autonomous driving, driver assistance system, car navigation systems. However, some errors often rose when 3D reconstructing due to the shade from the moving object in the road scene. The presented 3D reconstruction method with obstacle detection feedback can avoid this problem. Firstly, this paper offers a framework for the 3D reconstruction of road scene by laser scanning and vision. A calibration method based on the location of horizon is proposed, and a method of attitude angle measuring based on vanishing point is proposed to revise the 3D reconstruction result. Secondly, the reconstruction framework is extended by integrating with an object recognition that can automatically detect and discriminate obstacles in the input video streams by a RANSAC approach and threshold filter, and localizes them in the 3D model. 3D reconstruction and obstacle detection are tightly integrated and benefit from each other. The experiment result verified the feasibility and practicability of the proposed method.

Fuse -Replacement Noise-Filter Development for Automotive Audio–Video Navigation Systems Based on Loudness Isotropy Curves

2020 ◽  
Vol 25 (3) ◽  
pp. 83-92
Author(s):  
Bong-Seo Park ◽  
Hyun-cheol Park ◽  
Jung-jun Her
Keyword(s):  
Autonomous Driving ◽  
Navigation Systems ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Audio Quality ◽  
Noise Filter ◽  
Supply Noise ◽  
Low Pass ◽  
Assistance Systems ◽  
Audio Video

With the development of advanced driver assistance systems, the more reliable the autonomous driving technology is, the more the rest and entertainment times of the driver of the car increases. Hence, the importance of the entertainment function of automotive audio-video navigation (AVN) systems is increasing. Currently, the AVN system of automobiles has a monitoring function for fault diagnosis and a combination of functions. Applying these technologies is challenging for drivers who want to tune the audio quality to their musical taste. In this study, a method for upgrading the sound quality using a power supply noise filter without deforming the AVN system was developed. The low-pass attenuation that appeared as a side effect was solved by applying a filter using the loudness isotropic curve. In the installation method of the filter, the method of using a fuse holder minimized the inconvenience of AVN detachment and wiring. Based on the results obtained in this study, further research and improvement of the filter are required for audio tuning of various models.

A New Method for 3D Reconstruction of Headlight Contour

2013 ◽  
Vol 321-324 ◽  
pp. 862-867
Author(s):  
Fei Tao ◽  
Ping An Mu ◽  
Shu Guang Dai ◽  
Jia Xing Shen
Keyword(s):  
3D Reconstruction ◽  
Laser Scanning ◽  
Measurement Techniques ◽  
Three Dimensional ◽  
Contour Detection ◽  
Transformation Model ◽  
Laser Source ◽  
Reconstruction Method ◽  
Matching Effect ◽  
Geometry Model

This paper put forward a 3D reconstruction method of the headlight contours based on laser scanning technology and robotics. Firstly, according to the present three-dimensional measurement techniques, the article put forward a set of headlight contour detection method based on the analytic geometry model and the line laser source scanning principle. It establishes a 3D scanning model and coordinate transformation model for 3D reconstruction of the headlight contour. Secondly, according to the demanding accuracy it structures the 3D reconstruction system. Finally it realizes the 3D reconstruction of the headlight contour based on the method, and the result is tested and evaluated matching effect, the result shows that can effectively realize the 3D reconstruction of headlight contour and the method has a good stability.

scholarly journals Distraction Potential of Vehicle-Based On-Road Projection

2021 ◽  
Vol 11 (24) ◽  
pp. 12030
Author(s):  
Tobias Glück ◽  
Tobias Biermann ◽  
Alexander Wolf ◽  
Sören Budig ◽  
Arved Ziebehl ◽  
...  
Keyword(s):  
Eye Tracking ◽  
Tracking System ◽  
Autonomous Driving ◽  
The Other ◽  
Blind Study ◽  
Critical Threshold ◽  
Driver Assistance ◽  
The Road ◽  
Road Users ◽  
On The Road

With regard to autonomous driving, on-road projections cannot only be used for communication with the driver but also with other road users. Our study aims to investigate the distraction potential for other road users when on-road projections (e.g., for driver assistance) are used to communicate with the driver of the projecting vehicle. We perform this investigation in a blind study with 38 test persons who are overtaken six times on a constant motorway section by the projection vehicle. The distraction potential is examined with an eye-tracking system, which detects the direction of the subjects’ gaze. In addition, the subjects’ physiological perception of the headlight projection is recorded with a questionnaire afterward. Several test subjects looked at the projection for less than one second, which is well below the critical threshold for the distraction of 1.6 s. In the interviews, on the other hand, only one of the 38 test persons stated that a projection on the road was recognized. For the examined scenario, it is therefore deduced that on-road projections with the selected symbol shape and brightness do not lead to critical distraction.

A robot hand-eye calibration method of line laser sensor based on 3D reconstruction

2021 ◽  
Vol 71 ◽  
pp. 102136
Author(s):  
Mingyang Li ◽  
Zhijiang Du ◽  
Xiaoxing Ma ◽  
Wei Dong ◽  
Yongzhuo Gao
Keyword(s):  
3D Reconstruction ◽  
Calibration Method ◽  
Laser Sensor ◽  
Robot Hand

scholarly journals A Multi Camera and Multi Laser Calibration Method for 3D Reconstruction of Revolution Parts

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 765
Author(s):  
Hugo Álvarez ◽  
Marcos Alonso ◽  
Jairo R. Sánchez ◽  
Alberto Izaguirre
Keyword(s):  
3D Reconstruction ◽  
Calibration Method ◽  
Laser Plane ◽  
Calibration Methods ◽  
Laser Calibration ◽  
Calibration Object ◽  
Focus Plane ◽  
Chessboard Pattern ◽  
Line Patterns

This paper describes a method for calibrating multi camera and multi laser 3D triangulation systems, particularly for those using Scheimpflug adapters. Under this configuration, the focus plane of the camera is located at the laser plane, making it difficult to use traditional calibration methods, such as chessboard pattern-based strategies. Our method uses a conical calibration object whose intersections with the laser planes generate stepped line patterns that can be used to calculate the camera-laser homographies. The calibration object has been designed to calibrate scanners for revolving surfaces, but it can be easily extended to linear setups. The experiments carried out show that the proposed system has a precision of 0.1 mm.

scholarly journals Towards a Fully Automated 3D Reconstruction System Based on LiDAR and GNSS in Challenging Scenarios

2021 ◽  
Vol 13 (10) ◽  
pp. 1981
Author(s):  
Ruike Ren ◽  
Hao Fu ◽  
Hanzhang Xue ◽  
Zhenping Sun ◽  
Kai Ding ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
High Precision ◽  
Moving Objects ◽  
Autonomous Driving ◽  
Satellite System ◽  
Factor Graph ◽  
Mapping System ◽  
Global Navigation ◽  
Real World Datasets ◽  
Global Navigation Satellite

High-precision 3D maps play an important role in autonomous driving. The current mapping system performs well in most circumstances. However, it still encounters difficulties in the case of the Global Navigation Satellite System (GNSS) signal blockage, when surrounded by too many moving objects, or when mapping a featureless environment. In these challenging scenarios, either the global navigation approach or the local navigation approach will degenerate. With the aim of developing a degeneracy-aware robust mapping system, this paper analyzes the possible degeneration states for different navigation sources and proposes a new degeneration indicator for the point cloud registration algorithm. The proposed degeneracy indicator could then be seamlessly integrated into the factor graph-based mapping framework. Extensive experiments on real-world datasets demonstrate that the proposed 3D reconstruction system based on GNSS and Light Detection and Ranging (LiDAR) sensors can map challenging scenarios with high precision.

scholarly journals Time- and Resource-Efficient Time-to-Collision Forecasting for Indoor Pedestrian Obstacles Avoidance

2021 ◽  
Vol 7 (4) ◽  
pp. 61
Author(s):  
David Urban ◽  
Alice Caplier
Keyword(s):  
Neural Network ◽  
Autonomous Vehicles ◽  
Depth Estimation ◽  
Video Camera ◽  
Obstacle Detection ◽  
Navigation Systems ◽  
Time To Collision ◽  
Static Data ◽  
Monocular Depth ◽  
Fully Connected

As difficult vision-based tasks like object detection and monocular depth estimation are making their way in real-time applications and as more light weighted solutions for autonomous vehicles navigation systems are emerging, obstacle detection and collision prediction are two very challenging tasks for small embedded devices like drones. We propose a novel light weighted and time-efficient vision-based solution to predict Time-to-Collision from a monocular video camera embedded in a smartglasses device as a module of a navigation system for visually impaired pedestrians. It consists of two modules: a static data extractor made of a convolutional neural network to predict the obstacle position and distance and a dynamic data extractor that stacks the obstacle data from multiple frames and predicts the Time-to-Collision with a simple fully connected neural network. This paper focuses on the Time-to-Collision network’s ability to adapt to new sceneries with different types of obstacles with supervised learning.

scholarly journals Automatic Roadway Features Detection with Oriented Object Detection

2021 ◽  
Vol 11 (8) ◽  
pp. 3531
Author(s):  
Hesham M. Eraqi ◽  
Karim Soliman ◽  
Dalia Said ◽  
Omar R. Elezaby ◽  
Mohamed N. Moustafa ◽  
...  
Keyword(s):  
Object Detection ◽  
Safety Evaluation ◽  
Autonomous Driving ◽  
Detection Accuracy ◽  
The Road ◽  
Detection Model ◽  
Detection Approach ◽  
Roadway Safety ◽  
Safety Features ◽  
Oriented Object

Extensive research efforts have been devoted to identify and improve roadway features that impact safety. Maintaining roadway safety features relies on costly manual operations of regular road surveying and data analysis. This paper introduces an automatic roadway safety features detection approach, which harnesses the potential of artificial intelligence (AI) computer vision to make the process more efficient and less costly. Given a front-facing camera and a global positioning system (GPS) sensor, the proposed system automatically evaluates ten roadway safety features. The system is composed of an oriented (or rotated) object detection model, which solves an orientation encoding discontinuity problem to improve detection accuracy, and a rule-based roadway safety evaluation module. To train and validate the proposed model, a fully-annotated dataset for roadway safety features extraction was collected covering 473 km of roads. The proposed method baseline results are found encouraging when compared to the state-of-the-art models. Different oriented object detection strategies are presented and discussed, and the developed model resulted in improving the mean average precision (mAP) by 16.9% when compared with the literature. The roadway safety feature average prediction accuracy is 84.39% and ranges between 91.11% and 63.12%. The introduced model can pervasively enable/disable autonomous driving (AD) based on safety features of the road; and empower connected vehicles (CV) to send and receive estimated safety features, alerting drivers about black spots or relatively less-safe segments or roads.

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