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Fusion of Point Clouds for Obstacle Tracking during Airport Ground Operations

机译：融合点云以在机场地面运营期间进行障碍物跟踪

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During airport ground operations, one of the responsibilities of the pilots is to look out for obstructions. In large commercial aircraft, it can be challenging to observe certain regions, as the outside view from the cockpit seats can be limited. This becomes even worse in low visibility conditions and at night when objects are less noticeable. Failure to notice obstructions and to maintain an adequate distance of separation can lead to collisions. This paper addresses this issue by proposing an obstacle detection and tracking technology to assist pilots during taxi. Data, in the form of point clouds, is acquired from a stereo-vision system and a LIDAR sensor. It is then processed and analyzed in order to detect obstacles. Detected obstacles are fused and tracked with Particle Filters and Occupancy Grids. Finally, obstacle information is represented on a bird's eye view display. Experiments were carried out with the aircraft approaching a target obstacle and the performance of the system was assessed in different illumination and visibility conditions. The results obtained show that the system can reliably detect obstacles and represent them on the proposed display.
机译：在机场地面运营期间，飞行员的职责之一是注意障碍物。在大型商用飞机中，观察某些区域可能会很有挑战性，因为从驾驶舱座椅的外部视野可能会受到限制。在低能见度条件下以及夜间物体不那么明显的情况下，情况甚至更糟。如果没有发现障碍物并保持足够的间隔距离，可能会导致碰撞。本文通过提出一种障碍物检测和跟踪技术来协助滑行期间的飞行员，从而解决了这一问题。从立体视觉系统和LIDAR传感器获取点云形式的数据。然后对其进行处理和分析，以发现障碍。检测到的障碍物通过粒子过滤器和占用栅格进行融合和跟踪。最后，障碍物信息在鸟瞰图显示器上显示。在飞机接近目标障碍物的情况下进行了实验，并在不同的照明和可见度条件下评估了系统的性能。获得的结果表明，该系统可以可靠地检测障碍物并将其表示在建议的显示器上。

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