UAV obstacle avoidance using RGB-D system

Santos, Milton C. P.; Santana, Lucas Vago; Brandão, Alexandre Santos; Sarcinelli-Filho, Mário

doi:10.1109/icuas.2015.7152305

Cited by 29 publications

(11 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [35], the authors use an RGB-D camera indoor supervision system combined with an onboard IMU sensor to acquire the obstacle's position and measure the free space in the horizontal and vertical directions with respect to the current closest obstacles. The navigation control is straightforward.…”

Section: Related Workmentioning

confidence: 99%

A 3D Vision Cone Based Method for Collision Free Navigation of a Quadcopter UAV among Moving Obstacles

Ming

Huang

2021

Drones

View full text Add to dashboard Cite

In the near future, it’s expected that unmanned aerial vehicles (UAVs) will become ubiquitous surrogates for human-crewed vehicles in the field of border patrol, package delivery, etc. Therefore, many three-dimensional (3D) navigation algorithms based on different techniques, e.g., model predictive control (MPC)-based, navigation potential field-based, sliding mode control-based, and reinforcement learning-based, have been extensively studied in recent years to help achieve collision-free navigation. The vast majority of the 3D navigation algorithms perform well when obstacles are sparsely spaced, but fail when facing crowd-spaced obstacles, which causes a potential threat to UAV operations. In this paper, a 3D vision cone-based reactive navigation algorithm is proposed to enable small quadcopter UAVs to seek a path through crowd-spaced 3D obstacles to the destination without collisions. The proposed algorithm is simulated in MATLAB with different 3D obstacles settings to demonstrate its feasibility and compared with the other two existing 3D navigation algorithms to exhibit its superiority. Furthermore, a modified version of the proposed algorithm is also introduced and compared with the initially proposed algorithm to lay the foundation for future work.

show abstract

Section: Related Workmentioning

confidence: 99%

A 3D Vision Cone Based Method for Collision Free Navigation of a Quadcopter UAV among Moving Obstacles

Ming

Huang

2021

Drones

View full text Add to dashboard Cite

show abstract

“…Because UAV payloads and computing abilities are typically quite limited, detection devices to improve obstacle perception must be lightweight (e.g. monocular camera [89], stereo cameras [86], RGB-D cameras [90], Light Detection and Ranging (LIDAR) sensor [91], and ultrasonic distance sensors [92]). A typical UAV object detection system is shown in Fig.…”

Section: Obstacle Detection Systemmentioning

confidence: 99%

State of Technology Review of Civilian UAVs

Chen¹,

Laefer

Mangina³

2016

ENG

View full text Add to dashboard Cite

Background: Unmanned Aerial Vehicle (UAV) technology has exploded in recent years. Presently UAVs are beginning to be major in roads into geographical mapping, site inspection, agriculture, and search and rescue. Methods: This paper reviewed patents and papers worldwide related to both hard-ware and software for the construction and deployment of UAVs and is intended to provide a snapshot of currently available UAV technologies, as well as to identify recent trends and future opportunities in affiliated hardware and software. Results: Basic components related to self-designed units are explained (e.g. plat-form selection, autopilot control comparison and sensor selection), and current applications and research areas are discussed. Since autonomous navigation is a key technology in UAV applications, concepts about this are also explained. Conclusions: Both in the self-designed and commercial markets, UAV components are becoming modularized. By following a standard components list, it is no longer difficult to make a customised UAV. In this way, commercial products are becoming cheaper and more standardized in their performance. Current limitations of UAVs have also become more readily detectible. Extending the flight time, im-proving autonomous navigation abilities, and enriching the payload capacity will be the future research focus to address these limitations.

show abstract

“…The image contains a variety of information, so the most basic link to restore the real 3D scene from the image taken by UAV is image matching. At present, image matching is currently used extensively in target tracking [1][2][3][4], 3D reconstruction [5][6][7][8], visual SLAM [9][10][11], UAV obstacle avoidance navigation [12,13], and land surveying and mapping [14]. However, one of the common situations in real life is in a low-light environment photographed by UAV.…”

Section: Introductionmentioning

confidence: 99%

Fast Matching Method of UAV Aerial Photography Enhanced Low Illumination Image

Liu

et al. 2022

International Journal of Aerospace Engineering

View full text Add to dashboard Cite

Aiming at the problems of insufficient image contrast in three-dimensional reconstruction of UAV in low illumination environment and the unstable iteration times of the RANSAC algorithm in the feature matching process, real-time matching method of UAV aerial image is proposed. First, a new image enhancement algorithm is applied to the image to enhance its quality and visibility. Second, the enhanced fast algorithm in ORB extracts the feature points from the preprocessed image, and cross-matching performs rough matching. Finally, the PROSAC algorithm solves the homography matrix by selecting the highest quality interior points from the extracted feature points. To improve the matching accuracy, some exterior points that do not conform to the geometric characteristics of the image are removed based on the homography matrix and the set mismatch threshold. The results show that the improved ORB algorithm is applied to the low illumination environment of UAV aerial photography, the image matching accuracy in 3D reconstruction is improved, and the correct matching rate tends to 97.24~99.39%. The relevant research findings and conclusions provide a fast and effective method for UAV image matching in different low illumination environments.

show abstract

UAV obstacle avoidance using RGB-D system

Cited by 29 publications

References 22 publications

A 3D Vision Cone Based Method for Collision Free Navigation of a Quadcopter UAV among Moving Obstacles

A 3D Vision Cone Based Method for Collision Free Navigation of a Quadcopter UAV among Moving Obstacles

State of Technology Review of Civilian UAVs

Fast Matching Method of UAV Aerial Photography Enhanced Low Illumination Image

Contact Info

Product

Resources

About