Monocular vision-based obstacle detection/avoidance for unmanned aerial vehicles

2016 ◽  
Author(s):  
Abdulla Al-Kaff ◽  
Qinggang Meng ◽  
David Martin ◽  
Arturo de la Escalera ◽  
Jose Maria Armingol
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Obstacle Detection ◽  
Monocular Vision ◽  
Aerial Vehicles

Robust L∞ convex optimisation for UAVs cooperative motion estimation

2016 ◽  
Vol 231 (11) ◽  
pp. 2006-2031
Author(s):  
Mohammed Boulekchour ◽  
Nabil Aouf ◽  
Mark Richardson
Keyword(s):  
Motion Estimation ◽  
Unmanned Aerial Vehicles ◽  
Visual Motion ◽  
Monocular Vision ◽  
Computational Time ◽  
Vision Systems ◽  
Loop Closure ◽  
Cooperative Motion ◽  
Aerial Vehicles ◽  
Convex Optimisation

In this paper, a system for real-time cooperative monocular visual motion estimation with multiple unmanned aerial vehicles is proposed. Distributing the system across a network of vehicles allows for efficient processing in terms of both computational time and estimation accuracy. The resulting global cooperative motion estimation employs state-of-the-art approaches for optimisation, individual motion estimation and registration. Three-view geometry algorithms are developed within a convex optimisation framework on-board the monocular vision systems of each vehicle. In the presented novel distributed cooperative strategy a visual loop-closure module is deployed to detect any simultaneously overlapping fields of view of two or more of the vehicles. A positive feedback from the latter module triggers the collaborative motion estimation algorithm between any vehicles involved in this loop-closure. This scenario creates a flexible stereo set-up which jointly optimises the motion estimates of all vehicles in the cooperative scheme. Prior to that, vehicle-to-vehicle relative pose estimates are recovered with a novel robust registration solution in a global optimisation framework. Furthermore, as a complementary solution, a robust non-linear H∞filter is designed to fuse measurements from the vehicles’ on-board inertial sensors with the visual estimates. The proposed cooperative navigation solution has been validated on real-world data, using two unmanned aerial vehicles equipped with monocular vision systems.

Autonomous Area Exploration and Mapping in Underground Mine Environments by Unmanned Aerial Vehicles

Robotica ◽  
2019 ◽  
Vol 38 (3) ◽  
pp. 442-456 ◽  
Author(s):  
Hang Li ◽  
Andrey V. Savkin ◽  
Branka Vucetic
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Mathematical Proof ◽  
Obstacle Detection ◽  
Control Law ◽  
Sensing System ◽  
Aerial Vehicles ◽  
Multiple Uavs ◽  
Aerial Vehicle ◽  
Holonomic Robot ◽  
Flying Robot

SummaryIn this paper, we propose a method of using an autonomous flying robot to explore an underground tunnel environment and build a 3D map. The robot model we use is an extension of a 2D non-holonomic robot. The measurements and sensors we considered in the presented method are simple and valid in practical unmanned aerial vehicle (UAV) engineering. The proposed safe exploration algorithm belongs to a class of probabilistic area search, and with a mathematical proof, the performance of the algorithm is analysed. Based on the algorithm, we also propose a sliding control law to apply the algorithm to a real quadcopter in experiments. In the presented experiment, we use a DJI Guidance sensing system and an Intel depth camera to complete the localization, obstacle detection and 3D environment information capture. Furthermore, the simulations show that the algorithm can be implemented in sloping tunnels and with multiple UAVs.

scholarly journals An Embedded Platform for Positioning and Obstacle Detection for Small Unmanned Aerial Vehicles

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1175
Author(s):  
Salvatore Ponte ◽  
Gennaro Ariante ◽  
Umberto Papa ◽  
Giuseppe Del Core
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Degrees Of Freedom ◽  
Estimation Algorithm ◽  
General Purpose ◽  
Obstacle Detection ◽  
Position Estimation ◽  
Unmanned Aircraft ◽  
Measurement Unit ◽  
Accelerometer Data ◽  
Aerial Vehicles

Unmanned Aerial Vehicles (UAV) with on-board augmentation systems (UAS, Unmanned Aircraft System) have penetrated into civil and general-purpose applications, due to advances in battery technology, control components, avionics and rapidly falling prices. This paper describes the conceptual design and the validation campaigns performed for an embedded precision Positioning, field mapping, Obstacle Detection and Avoiding (PODA) platform, which uses commercial-off-the-shelf sensors, i.e., a 10-Degrees-of-Freedom Inertial Measurement Unit (10-DoF IMU) and a Light Detection and Ranging (LiDAR), managed by an Arduino Mega 2560 microcontroller with Wi-Fi capabilities. The PODA system, designed and tested for a commercial small quadcopter (Parrot Drones SAS Ar.Drone 2.0, Paris, France), estimates position, attitude and distance of the rotorcraft from an obstacle or a landing area, sending data to a PC-based ground station. The main design issues are presented, such as the necessary corrections of the IMU data (i.e., biases and measurement noise), and Kalman filtering techniques for attitude estimation, data fusion and position estimation from accelerometer data. The real-time multiple-sensor optimal state estimation algorithm, developed for the PODA platform and implemented on the Arduino, has been tested in typical aerospace application scenarios, such as General Visual Inspection (GVI), automatic landing and obstacle detection. Experimental results and simulations of various missions show the effectiveness of the approach.

scholarly journals Monocular Vision System for Fixed Altitude Flight of Unmanned Aerial Vehicles

Sensors ◽  
2015 ◽  
Vol 15 (7) ◽  
pp. 16848-16865 ◽  
Author(s):  
Kuo-Lung Huang ◽  
Chung-Cheng Chiu ◽  
Sheng-Yi Chiu ◽  
Yao-Jen Teng ◽  
Shu-Sheng Hao
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Vision System ◽  
Monocular Vision ◽  
Aerial Vehicles
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