Optical flow based system design for mobile robots

Güzel, Mehmet Serdar; Bicker, Robert

doi:10.1109/ramech.2010.5513134

Cited by 8 publications

(2 citation statements)

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“…Existing implementations are often a trade off between accuracy and efficiency and are restricted to mobile robots moving relatively slowly. For example, Low and Wyeth (2005) and Guzel and Bicker (2010) present experiments on the navigation of a wheeled mobile robotic platform using optical flow based TTC computation applied with an embedded conventional camera. Their softwares run at approximatively 5 Hz and the maximal speed of the mobile robot is limited to 0.2 m/s.…”

Section: Introductionmentioning

confidence: 99%

Asynchronous visual event-based time-to-contact

et al. 2014

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Reliable and fast sensing of the environment is a fundamental requirement for autonomous mobile robotic platforms. Unfortunately, the frame-based acquisition paradigm at the basis of main stream artificial perceptive systems is limited by low temporal dynamics and redundant data flow, leading to high computational costs. Hence, conventional sensing and relative computation are obviously incompatible with the design of high speed sensor-based reactive control for mobile applications, that pose strict limits on energy consumption and computational load. This paper introduces a fast obstacle avoidance method based on the output of an asynchronous event-based time encoded imaging sensor. The proposed method relies on an event-based Time To Contact (TTC) computation based on visual event-based motion flows. The approach is event-based in the sense that every incoming event adds to the computation process thus allowing fast avoidance responses. The method is validated indoor on a mobile robot, comparing the event-based TTC with a laser range finder TTC, showing that event-based sensing offers new perspectives for mobile robotics sensing.

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Section: Introductionmentioning

confidence: 99%

Asynchronous visual event-based time-to-contact

et al. 2014

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“…To reduce the computation cost of online FOE estimation, methods based on probability maps seem to be a promising alternative. Guzel et al [11] proposed to compute a probability map based on the amount of OF vector intersections per location. They demonstrated the performance of their method through a navigation task with a ground robot equipped with a camera.…”

Section: Introductionmentioning

confidence: 99%

FAITH: Fast iterative half-plane focus of expansion estimation using event-based optic flow

Dinaux,

Wessendorp,

Dupeyroux

et al. 2021

Preprint

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Course estimation is a key component for the development of autonomous navigation systems for robots. While state-of-the-art methods widely use visual-based algorithms, it is worth noting that they all fail to deal with the complexity of the real world by being computationally greedy and sometimes too slow. They often require obstacles to be highly textured to improve the overall performance, particularly when the obstacle is located within the focus of expansion (FOE) where the optic flow (OF) is almost null. This study proposes the FAst ITerative Half-plane (FAITH) method to determine the course of a micro air vehicle (MAV). This is achieved by means of an event-based camera, along with a fast RANSACbased algorithm that uses event-based OF to determine the FOE. The performance is validated by means of a benchmark on a simulated environment and then tested on a dataset collected for indoor obstacle avoidance. Our results show that the computational efficiency of our solution outperforms stateof-the-art methods while keeping a high level of accuracy. This has been further demonstrated onboard an MAV equipped with an event-based camera, showing that our event-based FOE estimation can be achieved online onboard tiny drones, thus opening the path towards fully neuromorphic solutions for autonomous obstacle avoidance and navigation onboard MAVs.

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