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.

Redefining Attitude Importance: The Possible Existence of Associative and Subjective Types of Importance Under a Global Definition

Traditionally, importance has been measured using subjective measures. The present thesis explores the possibility of a second type of importance, designated as “associative importance”. A new measure, the IIAT, was designed to capture the strength of association between an object and the attribute of importance. This thesis then evaluated the validity of the IIAT via an intervention paradigm in 2 studies, and by using the measure to predict a memory outcome in 2 other studies. Subjective measures of importance were also included in these studies and correlations between subjective measures and IIAT results were examined. Across all 4 studies, subjective-objective correlations were weak to modest and non-significant. The intervention studies provided promising evidence that interventions do affect associative importance as measured by the IIAT. The prediction studies provided somewhat mixed, but encouraging evidence that the IIAT may be able to predict memory performance. Notably, subjective measures were not able to predict memory performance at all, whereas the IIAT was able to predict some memory indices. Overall, there is some evidence supporting the existence of an associative importance construct, and that the IIAT provides valid results that are nonetheless different from that of subjective measures of attitude importance.

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.