Liz Murphy

Navigating, Recognizing and Describing Urban Spaces With Vision and Lasers

In this paper we describe a body of work aimed at extending the reach of mobile navigation and mapping. We describe how running topological and metric mapping and pose estimation processes concurrently, using vision and laser ranging, has produced a full six-degree-of-freedom outdoor navigation system. It is capable of producing intricate three-dimensional maps over many kilometers and in real time. We consider issues concerning the intrinsic quality of the built maps and describe our progress towards adding semantic labels to maps via scene de-construction and labeling. We show how our choices of representation, inference methods and use of both topological and metric techniques naturally allow us to fuse maps built from multiple sessions with no need for manual frame alignment or data association.

Building Large Scale Traversability Maps Using Vehicle Experience

Traversability maps are a global spatial representation of the relative difficulty in driving through a local region. These maps support simple optimisation of robot paths and have been very popular in path planning techniques. Despite the popularity of these maps, the methods for generating global traversability maps have been limited to using a-priori information. This paper explores the construction of large scale traversability maps for a vehicle performing a repeated activity in a boundedworking environment, such as a repeated delivery task.We evaluate the use of vehicle power consumption, longitudinal slip, lateral slip and vehicle orientation to classify the traversability and incorporate this into a map generated from sparse information.

Risky planning: Path planning over costmaps with a probabilistically bounded speed-accuracy tradeoff

This paper is about generating plans over uncertain maps quickly. Our approach combines the ALT (A* search, landmarks and the triangle inequality) algorithm and risk heuristics to guide search over probabilistic cost maps. We build on previous work which generates probabilistic cost maps from aerial imagery and use these cost maps to precompute heuristics for searches such as A* and D* using the ALT technique. The resulting heuristics are probability distributions. We can speed up and direct search by characterising the risk we are prepared to take in gaining search efficiency while sacrificing optimal path length. Results are shown which demonstrate that ALT provides a good approximation to the true distribution of the heuristic, and which show efficiency increases in excess of 70% over normal heuristic search methods.

Creating and using probabilistic costmaps from vehicle experience

Probabilistic costmaps provide a means of maintaining a representation of the uncertainty in the robots model of the environment; in contrast to the ubiquitous assumptive costmaps which abstract this uncertainty away. In this work we show for the first time how probabilistic costmaps can be learned in a self-supervised manner by a robot navigating in an outdoor environment. Traversability estimates garnered from onboard sensing are used in conjunction with colour information from a-priori available overhead imagery to extrapolate the traversability of locations previously traversed by the robot to a much larger area. Gaussian processes are used to predict the traversability at unknown locations in the 2D map, and a number of techniques to deal with heteroscedastic noise and varying confidence in the training data are evaluated. A prior technique to exploit the probabilistic nature of the map in a probabilistic heuristic for A* search demonstrates that planning over these maps can also be done efficiently.

Experimental Comparison of Odometry Approaches

Odometry is an important input to robot navigation systems, and we are interested in the performance of vision-only techniques. In this paper we experimentally evaluate and compare the performance of wheel odometry, monocular feature-based visual odometry, monocular patch-based visual odometry, and a technique that fuses wheel odometry and visual odometry, on a mobile robot operating in a typical indoor environment.

Choosing landmarks for risky planning

This work examines the effect of landmark placement on the efficiency and accuracy of risk-bounded searches over probabilistic costmaps for mobile robot path planning. In previous work, risk-bounded searches were shown to offer in excess of 70% efficiency increases over normal heuristic search methods. The technique relies on precomputing distance estimates to landmarks which are then used to produce probability distributions over exact heuristics for use in heuristic searches such as A* and D*. The location and number of these landmarks therefore influence greatly the efficiency of the search and the quality of the risk bounds. Here four new methods of selecting landmarks for risk based search are evaluated. Results are shown which demonstrate that landmark selection needs to take into account the centrality of the landmark, and that diminishing rewards are obtained from using large numbers of landmarks.