Single Camera Structure and Motion

Dani, Ashwin P.; Fischer, N.; Dixon, Warren E.

doi:10.1109/tac.2011.2162890

Cited by 100 publications

(61 citation statements)

References 20 publications

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“…However, accurate 3-D reconstruction of the terrain from monocular images is a very challenging problem [7]. Although recovering depth from single monocular images is an extensively studied research topic within the scientific community [21,22,23,24,25]; they fail to provide feasible solutions for planetary rovers because of the algorithmic complexity and required computational resources. For example structure from motion is a well known technique for the reconstruction of large uncontrolled environments using monocular cameras in standard 3-D computer vision applications.…”

Section: Terrain Perception Onboard Planetary Roversmentioning

confidence: 99%

Towards Camera-LIDAR Fusion-Based Terrain Modelling for Planetary Surfaces: Review and Analysis

Shaukat

Blacker

Spiteri

et al. 2016

Sensors

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In recent decades, terrain modelling and reconstruction techniques have increased research interest in precise short and long distance autonomous navigation, localisation and mapping within field robotics. One of the most challenging applications is in relation to autonomous planetary exploration using mobile robots. Rovers deployed to explore extraterrestrial surfaces are required to perceive and model the environment with little or no intervention from the ground station. Up to date, stereopsis represents the state-of-the art method and can achieve short-distance planetary surface modelling. However, future space missions will require scene reconstruction at greater distance, fidelity and feature complexity, potentially using other sensors like Light Detection And Ranging (LIDAR). LIDAR has been extensively exploited for target detection, identification, and depth estimation in terrestrial robotics, but is still under development to become a viable technology for space robotics. This paper will first review current methods for scene reconstruction and terrain modelling using cameras in planetary robotics and LIDARs in terrestrial robotics; then we will propose camera-LIDAR fusion as a feasible technique to overcome the limitations of either of these individual sensors for planetary exploration. A comprehensive analysis will be presented to demonstrate the advantages of camera-LIDAR fusion in terms of range, fidelity, accuracy and computation.

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Section: Terrain Perception Onboard Planetary Roversmentioning

confidence: 99%

Towards Camera-LIDAR Fusion-Based Terrain Modelling for Planetary Surfaces: Review and Analysis

Shaukat

Blacker

Spiteri

et al. 2016

Sensors

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“…One type of approaches assumes that the camera motion model is known in advanced or the related motion parameters can be measured [16], [17]. The other type of approaches uses the concept of optical flow, combing 2D histogram with Gaussian model or fuzzy genetic algorithm to track the environment features identified from the background and derive the affine transformation to eliminate the ego-motion of the background [18]- [23].…”

Section: Literature Surveymentioning

confidence: 99%

Voting-Based Motion Estimation for Real-Time Video Transmission in Networked Mobile Camera Systems

Lian¹,

Lin²,

Kuo³

et al. 2013

IEEE Trans. Ind. Inf.

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In a video-based surveillance system, a mobile camera can provide dynamical and wider monitoring range and the video data transmitted from cooperative mobile cameras can be used to actively detect the objects of interest. However, it is a difficult task to accurately detect the moving objects from the image frames captured by the mobile cameras and the data flow of surveillance video from multiple cameras could be huge. The camera motion usually causes the shifting of static background as well as the moving objects in the captured image frames. In order to correctly estimate the motion of moving objects, a voting-based motion estimation algorithm is proposed to process the image frames captured by the mobile camera. Based on the estimation, a content-based video transmission mechanism is then implemented to further effectively decrease encoding cost and bandwidth utilization. The overall approach consists of voting-based motion estimation, moving object edges detection and content-based sampling coding at temporal and spatial scales. Without knowing the prior knowledge of camera motion, the motion estimation algorithm only utilizes the shifting information of edges of static background to estimate the camera movement. The shifting information is determined based on the voting decision of several representative regions of interest and the estimated motion is then used to compensate for the visual content obtained from the captured image frames. The proposed algorithms have been experimentally tested on several practical scenarios and it is demonstrated that, under limited network bandwidth, the transmitted image quality can be progressively achieved and the transmission bandwidth utilization can be effectively decreased.

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“…an invertible mapping of the quantities to be estimated, and determines the observability conditions under which the estimation task can be solved. Other approaches sharing the same 'theoretical background' have also been presented in [3], [4], [5] under different conditions and/or attacking different SfM problems. Successful attempts to solve state estimation problems on non-Euclidean spaces can instead be found in [6], [7], while [8] discusses an observation algorithm able to recover in closed-form the unmeasurable states of interest.…”

Section: Introductionmentioning

confidence: 99%