Estimating the Heading of a Husky Mobile Robot with a LiDAR Compass based on Direction Maps

Without an absolute position sensor (e.g., GPS), an accurate heading estimate is necessary for proper localization of an autonomous unmanned vehicle or robot. This paper introduces direction maps (DMs), which represent the directions of only dominant surfaces of the vehicle’s environment and can be created with negligible effort. Given an environment with reoccurring surface directions (e.g., walls, buildings, parked cars), lines extracted from laser scans can be matched with a DM to provide an extremely lightweight heading estimate that is shown, through experimentation, to drastically reduce the growth of heading errors. The algorithm was tested using a Husky A200 mobile robot in a warehouse environment over traverses hundreds of metres in length. When a simple a priori DM was provided, the resulting heading estimation showed virtually no error growth.

Tectonic geomorphology assessment of the Ken River catchment near Panna, Madhya Pradesh, India

This thesis focuses on tectonic geomorphology and the response of the Ken River catchment to postulated tectonic forcing along a NE-striking monocline fold in the Panna region, Madhya Pradesh, India. Peninsular India is underlain by three northeast-trending paleotopographic ridges of Precambrian Indian basement, bounded by crustal-scale faults. Of particular interest is the Pokhara lineament, a crustal scale fault that defines the eastern edge of the Faizabad ridge, a paleotopographic high cored by the Archean Bundelkhand craton. The Pokhara lineament coincides with the monocline structure developed in the Proterozoic Vindhyan Supergroup rocks along the Bundelkhand cratonic margin. A peculiar, deeply incised meander-like feature, preserved along the Ken River where it flows through the monocline, may be intimately related to the tectonic regime of this system. This thesis examines 41 longitudinal stream profiles across the length of the monocline structure to identify any tectonic signals generated from recent surface uplift above the Pokhara lineament. It also investigates the evolution of the Ken River catchment in response to the generation of the monocline fold. Digital Elevation Models (DEM) from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were used to delineate a series of tributary watersheds and extract individual stream profiles which were imported into MATLAB for analysis. Regression limits were chosen to define distinct channel segments, and knickpoints were defined at breaks between channel segments where there was a discrete change in the steepness of the channel profile. The longitudinal channel profiles exhibit the characteristics of a fluvial system in transient state. There is a significant downstream increase in normalized steepness index in the channel profiles, as well as a general increase in concavity downstream, with some channels exhibiting convex, over-steepened segments. Normalized steepness indices and uppermost knickpoint elevations are on average much higher in streams along the southwest segment of the monocline compared to streams along the northeast segment. Most channel profiles have two to three knickpoints, predominantly exhibiting slope-break morphology. These data have important implications for recent surface uplift above the Pokhara lineament. Furthermore, geomorphic features preserved along the Ken River suggest that it is an antecedent river. The incised meander-like feature appears to be the abandoned river valley of a former Ken River course that was captured during the evolution of the landscape by what is the present day Ken River.

The LiDAR Compass: Extremely Lightweight Heading Estimation with Axis Maps

This paper introduces the LiDAR compass, a bounded and extremely lightweight heading estimation technique that combines a two-dimensional laser scanner and axis maps, which represent the orientations of flat surfaces in the environment. Although suitable for a variety of indoor and outdoor environments, the LiDAR compass is especially useful for embedded and real-time applications requiring low computational overhead. For example, when combined with a sensor that can measure translation (e.g., wheel encoders) the LiDAR compass can be used to yield accurate, lightweight, and very easily implementable localization that requires no prior mapping phase. The utility of using the LiDAR compass as part of a localization algorithm was tested on a widely-available open-source data set, an indoor environment, and a larger-scale outdoor environment. In all cases, it was shown that the growth in heading error was bounded, which significantly reduced the position error to less than 1% of the distance travelled.