scholarly journals NITCAD - Developing an object detection, classification and stereo vision dataset for autonomous navigation in Indian roads

2020 ◽  
Vol 171 ◽  
pp. 207-216
Author(s):  
Namburi GNVV Satya Sai Srinath ◽  
Athul Zac Joseph ◽  
S Umamaheswaran ◽  
Ch. Lakshmi Priyanka ◽  
Malavika Nair M ◽  
...  
Keyword(s):  
Object Detection ◽  
Stereo Vision ◽  
Autonomous Navigation

scholarly journals Assessing autonomous ship navigation using bridge simulators enhanced by cycle-consistent adversarial networks

2021 ◽  
pp. 1748006X2110210
Author(s):  
Andreas Brandsæter ◽  
Ottar L Osen
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Decision Support ◽  
Object Detection ◽  
Autonomous Navigation ◽  
Adversarial Networks ◽  
Maritime Navigation ◽  
Ship Navigation ◽  
Autonomous Ship ◽  
Ship Operation

The advent of artificial intelligence and deep learning has provided sophisticated functionality for sensor fusion and object detection and classification which have accelerated the development of highly automated and autonomous ships as well as decision support systems for maritime navigation. It is, however, challenging to assess how the implementation of these systems affects the safety of ship operation. We propose to utilize marine training simulators to conduct controlled, repeated experiments allowing us to compare and assess how functionality for autonomous navigation and decision support affects navigation performance and safety. However, although marine training simulators are realistic to human navigators, it cannot be assumed that the simulators are sufficiently realistic for testing the object detection and classification functionality, and hence this functionality cannot be directly implemented in the simulators. We propose to overcome this challenge by utilizing Cycle-Consistent Adversarial Networks (Cycle-GANs) to transform the simulator data before object detection and classification is performed. Once object detection and classification are completed, the result is transferred back to the simulator environment. Based on this result, decision support functionality with realistic accuracy and robustness can be presented and autonomous ships can make decisions and navigate in the simulator environment.

A survey on stereo vision-based autonomous navigation for multi-rotor MUAVs

Robotica ◽  
2018 ◽  
Vol 36 (8) ◽  
pp. 1225-1243 ◽  
Author(s):  
Jose-Pablo Sanchez-Rodriguez ◽  
Alejandro Aceves-Lopez
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Stereo Vision ◽  
Autonomous Navigation ◽  
Three Dimensional ◽  
Field Of View ◽  
Line Of Sight ◽  
Onboard Computer ◽  
Low Weight ◽  
Vision Based Navigation ◽  
Difficult Challenge

SUMMARYThis paper presents an overview of the most recent vision-based multi-rotor micro unmanned aerial vehicles (MUAVs) intended for autonomous navigation using a stereoscopic camera. Drone operation is difficult because pilots need the expertise to fly the drones. Pilots have a limited field of view, and unfortunate situations, such as loss of line of sight or collision with objects such as wires and branches, can happen. Autonomous navigation is an even more difficult challenge than remote control navigation because the drones must make decisions on their own in real time and simultaneously build maps of their surroundings if none is available. Moreover, MUAVs are limited in terms of useful payload capability and energy consumption. Therefore, a drone must be equipped with small sensors, and it must carry low weight. In addition, a drone requires a sufficiently powerful onboard computer so that it can understand its surroundings and navigate accordingly to achieve its goal safely. A stereoscopic camera is considered a suitable sensor because of its three-dimensional (3D) capabilities. Hence, a drone can perform vision-based navigation through object recognition and self-localise inside a map if one is available; otherwise, its autonomous navigation creates a simultaneous localisation and mapping problem.

scholarly journals A 3D Object Detection Based on Multi-Modality Sensors of USV

2019 ◽  
Vol 9 (3) ◽  
pp. 535 ◽  
Author(s):  
Yingying Wu ◽  
Huacheng Qin ◽  
Tao Liu ◽  
Hao Liu ◽  
Zhiqiang Wei
Keyword(s):  
Object Detection ◽  
Marine Environment ◽  
Autonomous Navigation ◽  
3D Object ◽  
Detection Depth ◽  
Generation Network ◽  
Institute Of Technology ◽  
3D Object Detection ◽  
Complicated Surface ◽  
Fusion Detection

Unmanned Surface Vehicles (USVs) are commonly equipped with multi-modality sensors. Fully utilized sensors could improve object detection of USVs. This could further contribute to better autonomous navigation. The purpose of this paper is to solve the problems of 3D object detection of USVs in complicated marine environment. We propose a 3D object detection Depth Neural Network based on multi-modality data of USVs. This model includes a modified Proposal Generation Network and Deep Fusion Detection Network. The Proposal Generation Network improves feature extraction. Meanwhile, the Deep Fusion Detection Network enhances the fusion performance and can achieve more accurate results of object detection. The model was tested on both the KITTI 3D object detection dataset (A project of Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago) and a self-collected offshore dataset. The model shows excellent performance in a small memory condition. The results further prove that the method based on deep learning can give good accuracy in conditions of complicated surface in marine environment.

A Stereo-Vision Based System for Autonomous Navigation of an In-Orbit Servicing Platform

2009 ◽  
Author(s):  
Giancarmine Fasano ◽  
Michele Grassi ◽  
Domenico Accardo
Keyword(s):  
Stereo Vision ◽  
Autonomous Navigation

1A1-H06 Autonomous Navigation of Hydraulically Actuated Hexapod Robot COMET-IV : The 3rd Report : Vertical Step Locomotion Control with Stereo Vision Camera

2008 ◽  
Vol 2008 (0) ◽  
pp. _1A1-H06_1-_1A1-H06_4
Author(s):  
Xiaowu LIN ◽  
Hiroshi OOROKU ◽  
Masaki OKU ◽  
Yuji HARADA ◽  
Kosuke FUTAGAMI ◽  
...  
Keyword(s):  
Stereo Vision ◽  
Autonomous Navigation ◽  
Hexapod Robot ◽  
Locomotion Control ◽  
Hydraulically Actuated ◽  
Vertical Step

scholarly journals Prediction of the scene quality for stereo vision-based autonomous navigation

2016 ◽  
Vol 49 (15) ◽  
pp. 94-99 ◽  
Author(s):  
Hélène Roggeman ◽  
Julien Marzat ◽  
Anthelme Bernard-Brunei ◽  
Guy Le Besnerais
Keyword(s):  
Stereo Vision ◽  
Autonomous Navigation

PROBABILISTIC APPROACH FOR AUTONOMOUS NAVIGATION USING STEREO VISION

2009 ◽  
Author(s):  
PUNEET KUMAR ◽  
NEHA KUMARI ◽  
SHIVANGI SRIVASTAVA
Keyword(s):  
Stereo Vision ◽  
Autonomous Navigation ◽  
Probabilistic Approach

scholarly journals Object Detection and 3D Position Estimation based on Stereo Vision

2017 ◽  
Vol 10 (4) ◽  
pp. 318-324
Author(s):  
Haengseon Son ◽  
Seonyoung Lee ◽  
Kyoungwon Min ◽  
Seongjin Seo
Keyword(s):  
Object Detection ◽  
Stereo Vision ◽  
Position Estimation
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