Visual SLAM with a RGB-D Camera on a Quadrotor UAV Using on-Board Processing

Aguilar, Wilbert G.; Rodríguez, Guillermo A.; Álvarez, Leandro; Sandoval, Sebastián; Quisaguano, Fernando J.; Limaico, Alex

doi:10.1007/978-3-319-59147-6_51

Cited by 35 publications

(11 citation statements)

References 29 publications

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“…As a future work, we extrapolate video freeze detection to onboard applications as people detection [28] [29], navigation [30] [31] [32], obstacle avoidance [33] [34] [35], and mapping [36].…”

Section: Discussionmentioning

confidence: 99%

Motion intention optimization for multirotor robust video stabilization

Aguilar

Ángulo

Pardo

2017

2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON)

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Abstract-In this paper we present an optimization algorithm for simultaneously detecting video freeze and obtaining the minimum number of the frame required in motion intention estimation for real time robust video stabilization on multirotor unmanned aerial vehicles. A combination of a filter and a threshold is used to the video freeze detection, and for optimizing the algorithm, we find the minimum number of frames for motion intention estimation without decrease the performance.

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Section: Discussionmentioning

confidence: 99%

Motion intention optimization for multirotor robust video stabilization

Aguilar

Ángulo

Pardo

2017

2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON)

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“…Representative applications include virtual reality and augmented reality fields, which are to render the virtual objects in the environment according to the map information and the current perspective information from SLAM, and the sense of reality of the virtual objects can be greatly enhanced. In the field of UAV, 126 SLAM can be used for map building, 127 autonomous obstacle avoidance, 128 and path planning. 129,130 In the unmanned vehicle field, SLAM technology provides visual function of odometer for mixing with other location techniques.…”

Section: Simultaneous Localization and Mappingmentioning

confidence: 99%

Advances in sensing and processing methods for three-dimensional robot vision

Chen

2018

International Journal of Advanced Robotic Systems

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In this article, we survey the recent developments of sensing methods in three-dimensional robot vision, centering on the current three-dimensional sensors and core techniques embedded in robotic systems. Over 8000 publications have reported rather wide application areas of three-dimensional robot vision in the last 40 years, such as human-robot interaction, object recognition, three-dimensional modeling, object tracking, searching and surveillance, as well as robot manipulation, localization, navigation, mapping, and path planning. Representative works and future research trends are also addressed in this article.

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“…These techniques use measurements from sensors like Kinect [6], Light Detection and Ranging (LIDAR), Sound Navigation and Ranging (SONAR), optical flow, stereo and monocular cameras for computation. SLAM algorithms utilize measurements from a single sensor [7] or a combination of sensors [8] to build or update a map of the environment surrounding the UAV while simultaneously using the same to estimate the UAV's position. The SfM approaches use measurements from sensors like optical flow [9] and/or a moving monocular camera [10] to determine depth map and the 3D structure.…”

Section: Introductionmentioning

confidence: 99%

Memory-Based Deep Reinforcement Learning for Obstacle Avoidance in UAV With Limited Environment Knowledge

Singla

Padakandla

Bhatnagar

2021

IEEE Trans. Intell. Transport. Syst.

177

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This paper presents our method for enabling a UAV quadrotor, equipped with a monocular camera, to autonomously avoid collisions with obstacles in unstructured and unknown indoor environments. When compared to obstacle avoidance in ground vehicular robots, UAV navigation brings in additional challenges because the UAV motion is no more constrained to a well-defined indoor ground or street environment. Horizontal structures in indoor and outdoor environments like decorative items, furnishings, ceiling fans, sign-boards, tree branches etc., also become relevant obstacles unlike those for ground vehicular robots. Thus, methods of obstacle avoidance developed for ground robots are clearly inadequate for UAV navigation. Current control methods using monocular images for UAV obstacle avoidance are heavily dependent on environment information. These controllers do not fully retain and utilize the extensively available information about the ambient environment for decision making. We propose a deep reinforcement learning based method for UAV obstacle avoidance (OA) and autonomous exploration which is capable of doing exactly the same. The crucial idea in our method is the concept of partial observability and how UAVs can retain relevant information about the environment structure to make better future navigation decisions. Our OA technique uses recurrent neural networks with temporal attention and provides better results compared to prior works in terms of distance covered during navigation without collisions. In addition, our technique has a high inference rate (a key factor in robotic applications) and is energy-efficient as it minimizes oscillatory motion of UAV and reduces power wastage.

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Visual SLAM with a RGB-D Camera on a Quadrotor UAV Using on-Board Processing

Cited by 35 publications

References 29 publications

Motion intention optimization for multirotor robust video stabilization

Motion intention optimization for multirotor robust video stabilization

Advances in sensing and processing methods for three-dimensional robot vision

Memory-Based Deep Reinforcement Learning for Obstacle Avoidance in UAV With Limited Environment Knowledge

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