An Embedded Optical Flow Processor for Visual Navigation using Optical Correlator Technology

Tchernykh, V.; Beck, Martín; Janschek, Klaus

doi:10.1109/iros.2006.282620

Cited by 8 publications

(7 citation statements)

References 20 publications

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“…A detailed sensitivity analysis within a window range from 8x8 to 64x64 pixels as shown an optimal window size of 24x24 pixels at a distance of 12 pixels (1/2 of correlated fragment size), making the best compromise between the accuracy/reliability of correlation and preservation of small details of the underlying 3D scene 14 . Expected performances of the optical flow processor have been estimated on the base of the conceptual design of the processor and results of simulation experiments, taking into account also the test results of the existing hardware models of the optical correlator made within previous studies [10][11] .…”

Section: Advanced Opto-electronic Optical Flow Processormentioning

confidence: 99%

Performance Analysis for Visual Planetary Landing Navigation Using Optical Flow and DEM Matching

Janschek

Beck

2006

AIAA Guidance, Navigation, and Control Conference and Exhibit

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Visual navigation for planetary landing vehicles shows many scientific and technical challenges due to inclined and rather high velocity approach trajectories, complex 3D environment and high computational requirements for real-time image processing. High relative navigation accuracy at landing site is required for obstacle avoidance and operational constraints. The current paper discusses detailed performance analysis results for a recently published concept of a visual navigation system, based on a mono camera as vision sensor and matching of the recovered and reference 3D models of the landing site. The recovered 3D models are being produced by real-time, instantaneous optical flow processing of the navigation camera images. An embedded optical correlator is introduced, which allows a robust and ultra high-speed optical flow processing under different and even unfavorable illumination conditions. The performance analysis is based on a detailed software simulation model of the visual navigation system, including the optical correlator as the key component for ultra-high speed image processing. The paper recalls the general structure of the navigation system and presents detailed end-to-end visual navigation performance results for a Mercury landing reference mission in terms of different visual navigation entry conditions, reference DEM resolution, navigation camera configuration and auxiliary sensor information.

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Section: Advanced Opto-electronic Optical Flow Processormentioning

confidence: 99%

Performance Analysis for Visual Planetary Landing Navigation Using Optical Flow and DEM Matching

Janschek

Beck

2006

AIAA Guidance, Navigation, and Control Conference and Exhibit

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“…Most of these visual systems were quite demanding in terms of their computational requirements and/or their weight or were not very well characterized, except for the optical mouse sensors [4], with which a standard error of approximately ±5 • /s around 25 • /s was obtained in the case of an optical mouse sensor measuring motion in a ±280 • /s overall range. However, to our knowledge, very few studies have been published so far in which optic flow systems have been implemented and tested outdoors onboard an unmanned aircraft subject to vibrations, where the illuminance cannot be easily controlled (see [1] in the case of linear 1-D motion sensors and see [18,20,27,49] in that of 2-D optic flow sensors). A particular effort has been made in this study to cope the sensor's measurement range [1.5 • /s; 25 • /s] with the one experienced during a lunar landing approach phase approximately of [2 • /s; 6 • /s].…”

Section: Introductionmentioning

confidence: 99%

Toward an Autonomous Lunar Landing Based on Low-Speed Optic Flow Sensors

Sabiron

Chavent

Burlion

et al. 2013

Advances in Aerospace Guidance, Navigation and Control

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For the last few decades, growing interest has returned to the quite challenging task of the autonomous lunar landing. Soft landing of payloads on the lunar surface requires the development of new means of ensuring safe descent with strong final conditions and aerospace-related constraints in terms of mass, cost and computational resources. In this paper, a two-phase approach is presented: first a biomimetic method inspired from the neuronal and sensory system of flying insects is presented as a solution to perform safe lunar landing. In order to design an autopilot relying only on optic flow (OF) and inertial measurements, an estimation method based on a two-sensor setup is introduced: these sensors allow us to accurately estimate the orientation of the velocity vector which is mandatory to control the lander's pitch in a quasi-optimal way with respect to the fuel consumption. Secondly a new low-speed Visual Motion Sensor (VMS) inspired by insects' visual systems performing local angular 1-D speed measurements ranging from 1.5 • /s to 25 • /s and weighing only 2.8 g is presented. It was tested under free-flying outdoor conditions over various fields onboard an 80 kg unmanned helicopter. These preliminary results show that the optic flow measured despite the complex disturbances encountered closely matched the ground-truth optic flow.

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“…Apollo 11). Newer approaches include lidar techniques [1], [2] and visual techniques [3], [4], [5], [6], [7], [8], [9] often supported with inertial measurements. In addition, vision-based navigation plays a key role when it is required to detect an extraterrestrial target from afar.…”

Section: Introductionmentioning

confidence: 99%

“…These systems either use visually assisted inertial navigation systems [25] or compute the optic flow by means of an optical correlator [3], [9] or extract information from a single camera [25], [26]. By contrast, the autopilot described here extends the previously described EMD-based OCTAVE-autopilot principles [27], [28] to a lunar lander.…”

Section: Introductionmentioning

confidence: 99%

Biomimetic optic flow sensing applied to a lunar landing scenario

Valette

Ruffier

Viollet

et al. 2010

2010 IEEE International Conference on Robotics and Automation

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IEEE International Conference on Robotics and Automation (ICRA), Anchorage, AK, MAY 03-08, 2010International audienceAutonomous landing on unknown extraterrestrial bodies requires fast, noise-resistant motion processing to elicit appropriate steering commands. Flying insects excellently master visual motion sensing techniques to cope with highly parallel data at a low energy cost, using dedicated motion processing circuits. Results obtained in neurophysiological, behavioural, and biorobotic studies on insect flight control were used to safely land a spacecraft on the Moon in a simulated environment. ESA's Advanced Concepts Team has identified autonomous lunar landing as a relevant situation for testing the potential applications of innovative bio-inspired visual guidance systems to space missions. Biomimetic optic flow-based strategies for controlling automatic landing were tested in a very realistic simulated Moon environment. Visual information was provided using the PANGU software program and used to regulate the optic flow generated during the landing of a two degrees of freedom spacecraft. The results of the simulation showed that a single elementary motion detector coupled to a regulator robustly controlled the autonomous descent and the approach of the simulated moonlander. ``Low gate'' located approximately 10 m above the ground was reached with acceptable vertical and horizontal speeds of 4 m/s and 5 m/s, respectively. It was also established that optic flow sensing methods can be used successfully to cope with temporary sensor blinding and poor lighting conditions

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An Embedded Optical Flow Processor for Visual Navigation using Optical Correlator Technology

Cited by 8 publications

References 20 publications

Performance Analysis for Visual Planetary Landing Navigation Using Optical Flow and DEM Matching

Performance Analysis for Visual Planetary Landing Navigation Using Optical Flow and DEM Matching

Toward an Autonomous Lunar Landing Based on Low-Speed Optic Flow Sensors

Biomimetic optic flow sensing applied to a lunar landing scenario

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