scholarly journals A Robust RANSAC-Based Planet Radius Estimation for Onboard Visual Based Navigation

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 4041
Author(s):  
Francesco de Gioia ◽  
Gabriele Meoni ◽  
Gianluca Giuffrida ◽  
Massimiliano Donati ◽  
Luca Fanucci
Keyword(s):  
Autonomous Navigation ◽  
Navigation Systems ◽  
Processing Pipeline ◽  
Ground Station ◽  
Noise Image ◽  
Simple Processing ◽  
Best Fitting ◽  
Body Center ◽  
Human Operators ◽  
Multiple Spacecraft

Individual spacecraft manual navigation by human operators from ground station is expected to be an emerging problem as the number of spacecraft for space exploration increases. Hence, as an attempt to reduce the burden to control multiple spacecraft, future missions will employ smart spacecraft able to navigate and operate autonomously. Recently, image-based optical navigation systems have proved to be promising solutions for inexpensive autonomous navigation. In this paper, we propose a robust image processing pipeline for estimating the center and radius of planets and moons in an image taken by an on-board camera. Our custom image pre-processing pipeline is tailored for resource-constrained applications, as it features a computationally simple processing flow with a limited memory footprint. The core of the proposed pipeline is a best-fitting model based on the RANSAC algorithm that is able to handle images corrupted with Gaussian noise, image distortions, and frame drops. We report processing time, pixel-level error of estimated body center and radius and the effect of noise on estimated body parameters for a dataset of synthetic images.

Geophysical Support of Magnetometer Autonomous Navigation Systems

2020 ◽  
Author(s):  
Vladimir T. Minligareev ◽  
Elena N. Khotenko ◽  
Vadim V. Tregubov ◽  
Tatyana V. Sazonova ◽  
Vaclav L. Kravchenok
Keyword(s):  
Autonomous Navigation ◽  
Navigation Systems

scholarly journals A Dynamic Calibration Method of Installation Misalignment Angles between Two Inertial Navigation Systems

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2947
Author(s):  
Ming Hua ◽  
Kui Li ◽  
Yanhong Lv ◽  
Qi Wu
Keyword(s):  
Autonomous Navigation ◽  
Inertial Navigation ◽  
Calibration Method ◽  
Measurement Information ◽  
Dynamic Calibration ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Additional Information ◽  
Two Systems ◽  
Dynamic Calibration Method

Generally, in order to ensure the reliability of Navigation system, vehicles are usually equipped with two or more sets of inertial navigation systems (INSs). Fusion of navigation measurement information from different sets of INSs can improve the accuracy of autonomous navigation effectively. However, due to the existence of misalignment angles, the coordinate axes of different systems are usually not in coincidence with each other absolutely, which would lead to serious problems when integrating the attitudes information. Therefore, it is necessary to precisely calibrate and compensate the misalignment angles between different systems. In this paper, a dynamic calibration method of misalignment angles between two systems was proposed. This method uses the speed and attitude information of two sets of INSs during the movement of the vehicle as measurements to dynamically calibrate the misalignment angles of two systems without additional information sources or other external measuring equipment, such as turntable. A mathematical model of misalignment angles between two INSs was established. The simulation experiment and the INSs vehicle experiments were conducted to verify the effectiveness of the method. The results show that the calibration accuracy of misalignment angles between the two sets of systems can reach to 1″ while using the proposed method.

Accelerating GPU Message Communication for Autonomous Navigation Systems

2021 ◽  
Author(s):  
Hao Wu ◽  
Jiangming Jin ◽  
Jidong Zhai ◽  
Yifan Gong ◽  
Wei Liu
Keyword(s):  
Autonomous Navigation ◽  
Navigation Systems ◽  
Message Communication

scholarly journals The Model and Training Algorithm of Compact Drone Autonomous Visual Navigation System

Data ◽  
2018 ◽  
Vol 4 (1) ◽  
pp. 4 ◽  
Author(s):  
Viacheslav Moskalenko ◽  
Alona Moskalenko ◽  
Artem Korobov ◽  
Viktor Semashko
Keyword(s):  
Autonomous Navigation ◽  
Search Algorithm ◽  
Visual Navigation ◽  
Frame Rate ◽  
Raspberry Pi ◽  
Navigation Systems ◽  
Training Algorithms ◽  
Translation Error ◽  
Deep Model ◽  
And Training

Trainable visual navigation systems based on deep learning demonstrate potential for robustness of onboard camera parameters and challenging environment. However, a deep model requires substantial computational resources and large labelled training sets for successful training. Implementation of the autonomous navigation and training-based fast adaptation to the new environment for a compact drone is a complicated task. The article describes an original model and training algorithms adapted to the limited volume of labelled training set and constrained computational resource. This model consists of a convolutional neural network for visual feature extraction, extreme-learning machine for estimating the position displacement and boosted information-extreme classifier for obstacle prediction. To perform unsupervised training of the convolution filters with a growing sparse-coding neural gas algorithm, supervised learning algorithms to construct the decision rules with simulated annealing search algorithm used for finetuning are proposed. The use of complex criterion for parameter optimization of the feature extractor model is considered. The resulting approach performs better trajectory reconstruction than the well-known ORB-SLAM. In particular, for sequence 7 from the KITTI dataset, the translation error is reduced by nearly 65.6% under the frame rate 10 frame per second. Besides, testing on the independent TUM sequence shot outdoors produces a translation error not exceeding 6% and a rotation error not exceeding 3.68 degrees per 100 m. Testing was carried out on the Raspberry Pi 3+ single-board computer.

Development of modularized in-pipe inspection robotic system: MRINSPECT VII+

Robotica ◽  
2021 ◽  
pp. 1-24
Author(s):  
Heesik Jang ◽  
Ho Moon Kim ◽  
Min Sub Lee ◽  
Yong Heon Song ◽  
Yoongeon Lee ◽  
...  
Keyword(s):  
Autonomous Navigation ◽  
Ccd Camera ◽  
Pipeline Inspection ◽  
Inspection Robot ◽  
Ground Station ◽  
Computing Unit ◽  
Differential Gear ◽  
Sensor Module ◽  
Communication Module ◽  
Gear Mechanism

Abstract This paper presents a modularized autonomous pipeline inspection robot called MRINSPECT VII+, which we recently developed. MRINSPECT VII+ is aimed at inspect in-service urban gas pipelines with a diameter of 200 mm. The robot consists of five basic modules: driving, sensing, joint, and battery modules. For nondestructive testing (NDT), an NDT module can be added to the system. The driving module uses a multiaxial differential gear mechanism to provide traction forces to the robot. The sensor module recognizes the pipeline element using position-sensitive detector (PSD) sensors and a CCD camera. The control module contains a computing unit and manages the robot’s autonomous navigation. The battery module supplies power to the system. Each module is connected via backdrivable active joint modules, which provide flexibility while moving inside narrow pipelines. Additionally, the wireless communication module helps the system communicate with the ground station. We tested MRINSPECT VII+ in real pipeline environments and validated its feasibility successfully.

scholarly journals Neural Network-Based Aircraft Conflict Prediction in Final Approach Maneuvers

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1708
Author(s):  
Rafael Casado ◽  
Aurelio Bermúdez
Keyword(s):  
Neural Network ◽  
Traffic Management ◽  
Autonomous Navigation ◽  
Decision Support Tool ◽  
Air Traffic ◽  
Navigation Systems ◽  
Support Tool ◽  
Final Approach ◽  
Aircraft Trajectories ◽  
Available Information

Conflict detection and resolution is one of the main topics in air traffic management. Traditional approaches to this problem use all the available information to predict future aircraft trajectories. In this work, we propose the use of a neural network to determine whether a particular configuration of aircraft in the final approach phase will break the minimum separation requirements established by aviation rules. To achieve this, the network must be effectively trained with a large enough database, in which configurations are labeled as leading to conflict or not. We detail the way in which this training database has been obtained and the subsequent neural network design and training process. Results show that a simple network can provide a high accuracy, and therefore, we consider that it may be the basis of a useful decision support tool for both air traffic controllers and airborne autonomous navigation systems.

scholarly journals Application of Updated Sage–Husa Adaptive Kalman Filter in the Navigation of a Translational Sprinkler Irrigation Machine

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1269 ◽  
Author(s):  
Kenan Liu ◽  
Wuyun Zhao ◽  
Bugong Sun ◽  
Pute Wu ◽  
Delan Zhu ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Autonomous Navigation ◽  
Sprinkler Irrigation ◽  
The Self ◽  
Maximum Deviation ◽  
Navigation Systems ◽  
Adaptive Kalman Filter ◽  
Average Deviation ◽  
Positioning Accuracy ◽  
Promising Development

Autonomous navigation for agricultural machinery has broad and promising development prospects. Kalman filter technology, which can improve positioning accuracy, is widely used in navigation systems in different fields. However, there has not been much research performed into navigation for sprinkler irrigation machines (SIMs). In this paper, firstly, a self-developed SIM is introduced. Secondly, the kinematics model is established on the platform of the self-developed SIM, and the updated Sage–Husa adaptive Kalman filter, which is an accurate and real-time self-adaptive filtering algorithm, is applied in the navigation of the SIM with the aim of improving the positioning accuracy. Finally, experiment verifications were carried out, and the results show that the self-developed SIM has good navigation performance. Besides this, the influence of abnormal observations on the positioning accuracy of the system can be restrained by using the updated Sage–Husa adaptive Kalman filter. After using the updated Sage–Husa adaptive Kalman filter for the SIM, the maximum deviation between the SIM and the predetermined path is 0.18 m, and the average deviation is 0.08 m; these deviations are within a reasonable range. This proves that the updated Sage–Husa adaptive Kalman filter is applicable for the navigation of sprinkler irrigation machines.

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