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Lab setup

Lab setup

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Abstract—Object detection and tracking is a challenging task, especially for unmanned aerial robots in complex environments where both static and dynamic objects are present. It is, however, essential for ensuring safety of the robot during navigation in such environments. In this work we present a practical online approach which is based on a 2D L...

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... Hokuyo UTM-30LX is mounted horizontally on the robot. Due to the limited space of our lab, the detection scope is limited to a rectangular space with size 1.9m x 2.9m. This experimental arena is surrounded by a wall made of nets from all sides, as shown in Fig. 5. LIDAR runs at a rate of 20 Hz during ...

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... Often these approaches are used as a last resort. [21,22] Yasin et al. [10] presents a survey where they explain state-of-art techniques on collision avoidance. Their in-depth review provides a thorough understanding of the various approaches available. ...
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... Moreover, it is unsuitable for practical applications. Voos et al. (2018) investigated the detection and recognition of obstacles in complex and changing environments, using rapid commands through the autonomous judgment of flight control systems for collision-free navigation. Lobo et al. (2018) developed three-dimensional models to estimate drone trajectories using predictive models, to simulate wind speeds, and to calculate the likelihood of flight direction, with adjustment of the flight attitude to reduce crash probability. ...
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... The velocity constraint is a metric that takes the velocity of the obstacles in consideration. Sense and avoid and geometric approaches are capable to handle this constraint well [7], [8]. However, force-field and optimisation methods are better for predefined planning which does not take changing dynamics into consideration [4, p. 11]. ...
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... En [11], los autores proponen el uso de un LIDAR 2D para detectar obstáculos. Su método es capaz de distinguir entre obstáculos estáticos y dinámicos, así como de seguir objetos dinámicos. ...
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Looming, traditionally defined as the relative expansion of objects in the observer's retina, is a fundamental visual cue for perception of threat and can be used to accomplish collision free navigation. The measurement of the looming cue is not only limited to vision, and can also be obtained from range sensors like LiDAR (Light Detection and Ranging). In this article we present two methods that process raw LiDAR data to estimate the looming cue. Using looming values we show how to obtain threat zones for collision avoidance tasks. The methods are general enough to be suitable for any six-degree-of-freedom motion and can be implemented in real-time without the need for fine matching, point-cloud registration, object classification or object segmentation. Quantitative results using the KITTI dataset shows advantages and limitations of the methods.
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