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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Abstract-We describe a sensor network deployment method using autonomous flying robots. Such networks are suitable for tasks such as large-scale environmental monitoring or for command and control in emergency situations. We describe in detail the algorithms used for deployment and for measuring network connectivity and provide experimental data we collected from field trials. A particular focus is on determining gaps in connectivity of the deployed network and generating a plan for a second, repair, pass to complete the connectivity. This project is the result of a collaboration between three robotics labs (CSIRO, USC, and Dartmouth.) I. INTRODUCTIONWe investigate the role of mobility in sensor networks. Mobility can be used to deploy sensor networks, to maintain and repair connectivity, and to enable applications such as monitoring and surveillance. We examine sensor networks that consist of static and dynamic nodes. The static sensor nodes are "Motes" and the mobile nodes are autonomous helicopters. Integrating static nodes with mobile robots enhances the capabilities of both types of devices and enables new applications. Using networking, the sensors can provide the Unmanned Aerial Vehicle (UAV) with information which is out of the range of the robot. Using mobility, the robot can deploy the network, localize the nodes in the network, maintain connectivity by introducing new nodes as needed, and and act as "data mules" to relay information between disconnected wireless clouds.We combine ad-hoc networking, sensing, and control to deploy and use a sensor network. We use an autonomous helicopter to deploy a sensor network with a controlled topology, for example a star, grid, or random. The helicopter deploys the sensors one at a time at designated locations. Once on the ground, the sensors establish an ad-hoc network and compute their connectivity map in a localized and distributed way.The helicopter is equipped with a sensor node so that it is a mobile component of the sensor network and it can communicate to the ground. This system can handle on-demand node deployment. The connectivity map is used to determine ground locations that require additional nodes (for example to repair connectivity or to increase bandwidth). The helicopter responds by flying to that location and deploying a new

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The recent explosion in sub‐meter resolution airborne LiDAR data raises the possibility of mapping detailed changes to Earth's topography. We present a new method that determines three‐dimensional (3‐D) coseismic surface displacements and rotations from differencing pre‐ and post‐earthquake airborne LiDAR point clouds using the Iterative Closest Point (ICP) algorithm. Tested on simulated earthquake displacements added to real LiDAR data along the San Andreas Fault, the method reproduces the input deformation for a grid size of ∼50 m with horizontal and vertical accuracies of ∼20 cm and ∼4 cm, values that mimic errors in the original spot height measurements. The technique also measures rotations directly, resolving the detailed kinematics of distributed zones of faulting where block rotations are common. By capturing near‐fault deformation in 3‐D, the method offers new constraints on shallow fault slip and rupture zone deformation, in turn aiding research into fault zone rheology and long‐term earthquake repeatability.

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