We present the design and implementation of a real-time, vision-based landing algorithm for an autonomous helicopter. The landing algorithm is integrated with algorithms for visual acquisition of the target (a helipad) and navigation to the target from an arbitrary initial position and orientation. We use vision for precise target detection and recognition and a combination of vision and GPS for navigation. The helicopter updates its landing target parameters based on vision and uses an on board behaviorbased controller to follow a path to the landing site. We present significant results from flight trials in the field which demonstrate that our detection, recognition and control algorithms are accurate, robust and repeatable.

Structure from Motion (SfM) generates high-resolution topography and coregistered texture (color) from an unstructured set of overlapping photographs taken from varying viewpoints, overcoming many of the cost, time, and logistical limitations of Light Detection and Ranging (LiDAR) and other topographic surveying methods. This paper provides the fi rst investigation of SfM as a tool for mapping fault zone topography in areas of sparse or low-lying vegetation. First, we present a simple, affordable SfM workfl ow, based on an unmanned helium balloon or motorized glider, an inexpensive camera, and semiautomated software. Second, we illustrate the system at two sites on southern California faults covered by existing airborne or terrestrial LiDAR, enabling a comparative assessment of SfM topography resolution and precision. At the fi rst site, an ~0.1 km 2 alluvial fan on the San Andreas fault, a colored point cloud of density mostly >700 points/m 2 and a 3 cm digital elevation model (DEM) and orthophoto were produced from 233 photos collected ~50 m above ground level. When a few global positioning system ground control points are incorporated, closest point vertical distances to the much sparser (~4 points/m 2) airborne LiDAR point cloud are mostly <3 cm. The second site spans an ~1 km section of the 1992 Landers earthquake scarp. A colored point cloud of density mostly >530 points/m 2 and a 2 cm DEM and orthophoto were produced from 450 photos taken from ~60 m above ground level. Closest point vertical distances to existing terrestrial LiDAR data of comparable density are mostly <6 cm. Each SfM survey took ~2 h to complete and several hours to generate the scene topography and texture. SfM greatly facilitates the imaging of subtle geomorphic offsets related to past earthquakes as well as rapid response mapping or long-term monitoring of faulted landscapes.

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