Patrick McGarey

Rapid mapping of ultrafine fault zone topography with structure from motion

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 first 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 workflow, 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 first 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 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

A 16-channel flex circuit for cryogenic microwave signal transmission

Heterodyne focal plane arrays used in the terahertz (THz) regime currently require a discrete set of rigid coaxial cables for the transmission of individual intermediate frequency (IF) signals. Consequently, the size of an array is limited to ~10s of pixels due to limited physical space and the complexity of assembly. In order to achieve an array with ~1000 pixels or greater, new interconnections must be developed capable of carrying multiple IF signals on a single carrier which is flexible, robust to noise, and terminated with a high density RF connector. As an intermediate step to the development of a ~1000 pixel heterodyne focal plane array, the Kilopixel Array Pathfinder Project (KAPPa) has developed a 16 channel IF flex circuit. Initially, design simulations were performed to evaluate various means of high-frequency (1~10 GHz) signal transmission, including microstrip, stripline and coplanar waveguides. The method allowing for the closest signal spacing and greatest resistance to radio frequency interference (RFI) was determined to be stripline. Designs were considered where stripline transitioned to microstrip in order to terminate the signal. As microstrip transmission lines are sensitive to RFI, a design featuring just stripline was evaluated. In both the stripline-to-microstrip and stripline-only designs, a three-layer copper-coated polyimide substrate was used. Signal transitions were accomplished by a signal carrying “hot” via passing through a series of three conductive pads, similar to work by Leib et al. (2010). The transition design essentially mimics a coaxial line, where the radial distance between the pads and the ground plane is optimized in order to achieve desired impedances. In simulation, 50 Ohm impedances were achieved throughout, with crosstalk and return loss limited to -30dB. Terminations are made via an array of Corning Gilbert G3PO blind mate connectors, which are small enough to match the 6mm pixel pitch of the KAPPa focal plane unit. In addition, circuits with SMA terminations were designed to enable straightforward testing with a vector network analyzer (VNA). Initial designs use ½ oz. (18 microns thickness) copper conductors. In the KAPPa application, the copper conductor is still suitable for cryogenic applications because of the very small cross section presented by the copper conductor. The stripline design allows the interconnect to be clamped securely for heat sinking with a copper clamp at 10K and 60K. Heat load to the 4K stage is limited to 10 mW if the circuit is heat sunk at 10K 150mm from the 4K focal plane. Future designs could be implemented with phosphor bronze as the conductor to further limit heat load at the expense of added loss.

System Design of a Tethered Robotic Explorer (TReX) for 3D Mapping of Steep Terrain and Harsh Environments

The use of a tether in mobile robotics provides a method to safely explore steep terrain and harsh environments considered too dangerous for humans and beyond the capability of standard ground rovers. However, there are significant challenges yet to be addressed concerning mobility while under tension, autonomous tether management, and the methods by which an environment is assessed. As an incremental step towards solving these problems, this paper outlines the design and testing of a center-pivoting tether management payload enabling a four-wheeled rover to access and map steep terrain. The chosen design permits a tether to attach and rotate passively near the rover’s center-of-mass in the direction of applied tension. Prior design approaches in tethered climbing robotics are presented for comparison. Tests of our integrated payload and rover, Tethered Robotic Explorer (TReX), show full rotational freedom while under tension on steep terrain, and basic autonomy during flat-ground tether management. Extensions for steep-terrain tether management are also discussed. Lastly, a planar lidar fixed to a tether spool is used to demonstrate a 3D mapping capability during a tethered traverse. Using visual odometry to construct local point-cloud maps over short distances, a globally-aligned 3D map is reconstructed using a variant of the Iterative Closest Point (ICP) algorithm.

The line leading the blind: Towards nonvisual localization and mapping for tethered mobile robots

Mobile robots supported by an electromechanical tether can safely explore extremely rugged terrain in resource-limited environments. While a tether provides power, wired communication, and support on steep surfaces, it also reduces maneuverability; in cluttered environments the tether will contact obstacles, forming intermediate anchor points. In order for the robot to avoid tether entanglement, it must localize itself with respect to any added anchor points. Accordingly, we present a first approach towards nonvisual localization and mapping that utilizes tether measurements and wheel odometry to jointly estimate vehicle trajectory and tether-to-obstacle contact points. The proposed method is inspired by FastSLAM, where instead of updating a map of landmarks, tether length and bearing measurements are used to update sequential lists of anchor points for every particle representing a belief of the robots trajectory. Results from both simulation and experiment using our Tethered Robotic eXplorer (TReX) demonstrate that (i) our method is more accurate than odometry alone, and (ii) we are able to map intermediate anchor points nonvisually.

AUTOKITE - Experimental Use of a Low Cost Autonomous Kite Plane for Aerial Photography and Reconnaissance.

An experimental kite-plane capable of autonomous aerial imaging is introduced as a viable low-cost small-scale civilian UAV imaging platform ideal for field use. The AUTOKITE fulfills a need currently unmet by other fully automated Unmanned Aerial Vehicles (UAVs), resulting from ease of operation, extended flight time, and overall reliability. The AUTOKITE is outfitted with an off-the-shelf autopilot system, and has demonstrated fully autonomous flight in field deployments while collecting high-resolution (~12 cm/pixel) images. The AUTOKITE has been used to map regions historically prone to earthquakes along the Southern San Andreas Fault in California. Comparative image methods enabled by photogrammetric software, like Agisofts PhotoScan, are then used to discern Structure-from-Motion (SfM) from a multitude of aerial images taken by AUTOKITE [8]. Processing SfM data from overlapping images results in the creation of Digital Elevation Models (DEMs) and Orthophotos for geographic areas of interest. In addition to sample data sets illustrating the SfM process, The AUTOKITE is compared with three alternative UAV systems, and payload integration/automation details are discussed.

Autokite experimental use of a low cost autonomous kite plane for aerial photography and reconnaissance

An experimental kite-plane capable of autonomous aerial imaging is introduced as a viable low-cost small-scale civilian UAV imaging platform ideal for field use. The AUTOKITE fulfills a need currently unmet by other fully automated Unmanned Aerial Vehicles (UAVs), resulting from ease of operation, extended flight time, and overall reliability. The AUTOKITE is outfitted with an off-the-shelf autopilot system, and has demonstrated fully autonomous flight in field deployments while collecting high-resolution (~12 cm/pixel) images. The AUTOKITE has been used to map regions historically prone to earthquakes along the Southern San Andreas Fault in California. Comparative image methods enabled by photogrammetric software, like Agisofts PhotoScan, are then used to discern Structure-from-Motion (SfM) from a multitude of aerial images taken by AUTOKITE [8]. Processing SfM data from overlapping images results in the creation of Digital Elevation Models (DEMs) and Orthophotos for geographic areas of interest. In addition to sample data sets illustrating the SfM process, The AUTOKITE is compared with three alternative UAV systems, and payload integration/automation details are discussed.

The Kilopixel Array Pathfinder Project (KAPPa), a 16 pixel integrated heterodyne focal plane array

KAPPa (the Kilopixel Array Pathfinder Project) is developing key technologies to enable the construction of heterodyne focal plane arrays in the terahertz frequency regime with ~1000 pixels. The leap to ~1000 pixels requires solutions to several key technological problems before the construction of such a focal plane is possible. The KAPPa project will develop a small (16-pixel) 2D integrated heterodyne focal plane array for the 660 GHz atmospheric window as a technological pathfinder towards future kilopixel heterodyne focal plane arrays.

Falling in line: Visual route following on extreme terrain for a tethered mobile robot

This paper describes visual route following for a cliff-climbing, tethered mobile robot for the purpose of autonomously traversing extreme terrain in the presence of obstacles. When the robots tether contacts an obstacle, an intermediate anchor is formed. In order to detach from intermediate anchors and avoid entanglement, the robot must backtrack along its outgoing trajectory. We use the Visual Teach & Repeat (VT&R) algorithm to autonomously repeat a manually taught path. However, our problem is complicated by the fact that the robots tether must (i) remain taut regardless of inclination, (ii) allow the robot to drive freely, and (iii) provide motion assistance when wheel traction is reduced on steep slopes. To enable visual route following over varied terrain, we have developed a novel tether controller that selects a safe, steady-state tension based on the robots inclination while also accounting for vehicle motion. Experiments are performed on our Tethered Robotic Explorer (TReX), which autonomously repeats paths while tethered in both flat-indoor and steep-outdoor environments in the presence of obstacles.

Field Deployment of the Tethered Robotic eXplorer to Map Extremely Steep Terrain

Mobile robots outfitted with a supportive tether are ideal for gaining access to extreme environments for mapping when human or remote observation is not possible. This paper details a field deployment with the (TReX) to map a steep, tree-covered rock outcrop in an open-pit gravel mine. TReX is a mobile robot designed for the purpose of mapping extremely steep and cluttered environments for geologic and infrastructure inspection. Mapping is accomplished with a 2D lidar fixed to an actuated tether spool, which rotates to produce a 3D scan only when the robot drives and manages its tether. In order to handle motion distortion, we evaluate two existing, real-time approaches to estimate the trajectory of the robot and rectify individual scans before alignment into the map: (i) a continuous-time, lidar-only approach that handles asynchronous measurements using a physically motivated, constant-velocity motion prior, and (ii) a method that computes visual odometry from streaming stereo images to use as a motion estimate during scan collection.Once rectified, individual scans are matched to the global map by an efficient variant of the ICP algorithm. Our results include a comparison of estimated maps and trajectories to ground truth (measured by a remote survey station), an example of mapping in highly cluttered terrain, and lessons learned from the deployment and continued development of TReX.

TSLAM: Tethered simultaneous localization and mapping for mobile robots:

Tethered mobile robots are useful for exploration in steep, rugged, and dangerous terrain. A tether can provide a robot with robust communications, power, and mechanical support, but also constrains motion. In cluttered environments, the tether will wrap around a number of intermediate ‘anchor points’, complicating navigation. We show that by measuring the length of tether deployed and the bearing to the most recent anchor point, we can formulate a tethered simultaneous localization and mapping (TSLAM) problem that allows us to estimate the pose of the robot and the positions of the anchor points, using only low-cost, nonvisual sensors. This information is used by the robot to safely return along an outgoing trajectory while avoiding tether entanglement. We are motivated by TSLAM as a building block to aid conventional, camera, and laser-based approaches to simultaneous localization and mapping (SLAM), which tend to fail in dark and or dusty environments. Unlike conventional range-bearing SLAM, the TSLAM problem must account for the fact that the tether-length measurements are a function of the robot’s pose and all the intermediate anchor-point positions. While this fact has implications on the sparsity that can be exploited in our method, we show that a solution to the TSLAM problem can still be found and formulate two approaches: (i) an online particle filter based on FastSLAM and (ii) an efficient, offline batch solution. We demonstrate that either method outperforms odometry alone, both in simulation and in experiments using our TReX (Tethered Robotic eXplorer) mobile robot operating in flat-indoor and steep-outdoor environments. For the indoor experiment, we compare each method using the same dataset with ground truth, showing that batch TSLAM outperforms particle-filter TSLAM in localization and mapping accuracy, owing to superior anchor-point detection, data association, and outlier rejection.