Carnegie Mellon Robotics Institute
| Assessing a landing zone (LZ) reliably is essential for safe operation of vertical takeoﬀ
and landing (VTOL) aerial vehicles that land at unimproved locations. Currently an
operator has to rely on visual assessment to make an approach decision; however. visual
information from afar is insuﬃcient to judge slope and detect small obstacles. Prior work
has modeled LZ quality based on plane ﬁtting, which only partly represents the interaction
between vehicle and ground.
Our approach consists of a coarse evaluation based on slope and roughness criteria, a ﬁne evaluation for skid contact, and body clearance of a location. We investigated whether the evaluation is correct for using terrain maps collected from a helicopter. This paper deﬁnes the problem of evaluation, describes our incremental real-time algorithm, and discusses the eﬀectiveness of our approach.
In results from urban and natural environments, we were able to successfully classify LZs from point cloud maps collected on a helicopter. The presented method enables detailed assessment of LZs without an landing approach, thereby improving safety. Still, the method assumes low-noise point cloud data. We intend to increase robustness to outliers while still detecting small obstacles in future work.
Number of pages: 14
|Sebastian Scherer, Lyle J. Chamberlain, and Sanjiv Singh, "Online Assessment of Landing Sites," AIAA Infotech@Aerospace 2010 , April, 2010.|
author = "Sebastian Scherer and Lyle J. Chamberlain and Sanjiv Singh",
title = "Online Assessment of Landing Sites",
booktitle = "AIAA Infotech@Aerospace 2010 ",
month = "April",
year = "2010",
|The Robotics Institute is part of the School of Computer Science, Carnegie Mellon University.|
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