Efficient goal directed navigation using RatSLAM

RatSLAM is a system for vision based Simultaneous Localization and Mapping (SLAM) that has been shown to be capable of building stable representations of real world environments. In this paper we describe a method for using RatSLAM representations as the basis for navigation to designated goal locations. The method uses a new component, goal memory, to learn the temporal gradient between places. Paths are recalled or inferred from the goal memory by following the temporal gradient from the robot’s current position to the goal location. Experimental results have been gathered in a combined office and laboratory environment using a Pioneer robot. The experiments show that the robot can perform vision based SLAM on-line and in real time, and then use those representations immediately to navigate directly to designated goal locations.

Networked robots : flying robot navigation using a sensor net

This paper introduces the application of a sensor network to navigate a flying robot. We have developed distributed algorithms and efficient geographic routing techniques to incrementally guide one or more robots to points of interest based on sensor gradient fields, or along paths defined in terms of Cartesian coordinates. The robot itself is an integral part of the localization process which establishes the positions of sensors which are not known a priori. We use this system in a large-scale outdoor experiment with Mote sensors to guide an autonomous helicopter along a path encoded in the network. A simple handheld device, using this same environmental infrastructure, is used to guide humans.

Low-cost flight control system for a small autonomous helicopter

In this paper we describe a low-cost flight control system for a small (60 class) helicopter which is part of a larger project to develop an autonomous flying vehicle. Our approach differs from that of others in not using an expensive inertial/GPS sensing system. The primary sensors for vehicle stabilization are a low-cost inertial sensor and a pair of CMOS cameras. We describe the architecture of our flight control system, the inertial and visual sensing subsystems and present some flight control results.

PIPP #1 - Pedestrian Interaction Patch Project

The Pedestrian Interaction Patch Project (PIPP) seeks to exert influence over and encourage abnormal pedestrian behavior. By placing an unadvertised (and non recording) interactive video manipulation system and projection source in a high traffic public area, the PIPP allows pedestrians to privately (and publically) re-engage with a previously inactive physical environment, like a commonly used walkway or corridor. This system, the results of which are projected in real time on the architectural surface, inadvertently provides pedestrians with questions around preconceived notions of self and space. In an attempt to re-activate our relationship with the physical surrounds we occupy each day the PIPP creates a new set of memories to be recalled as we re-enter known environments once PIPP has moved on and as such re-enlivens our relationship with the everyday architecture we stroll past everyday. The PIPP environment is controlled using the software program Isadora, devised by Mark Coniglio at Troika Ranch, and contains a series of video manipulation patches that are designed to not only grab the pedestrians attention but to also encourage a sense of play and interaction between the architecture, the digital environment, the initially unsuspecting participant(s) and the pedestrian audience. The PIPP was included as part of the planned walking tour for the “Playing in Urban Spaces” seminar day, and was an installation that ran for the length of the symposium in a reclaimed pedestrian space that was encountered by both the participants and general public during the course of the day long event. Ideally once discovered PIPP encouraged pedestrians to return through the course of the seminar day to see if the environmental patches had changed or altered, and changed their standard route to include the PIPP installation or to avoid it, either way, encouraging an active response to the pathways normally traveled or newly discovered each day.

Theory and development of GPS integrity monitoring system

This thesis discusses various aspects of the integrity monitoring of GPS applied to civil aircraft navigation in different phases of flight. These flight phases include en route, terminal, non-precision approach and precision approach. The thesis includes four major topics: probability problem of GPS navigation service, risk analysis of aircraft precision approach and landing, theoretical analysis of Receiver Autonomous Integrity Monitoring (RAIM) techniques and RAIM availability, and GPS integrity monitoring at a ground reference station. Particular attention is paid to the mathematical aspects of the GPS integrity monitoring system. The research has been built upon the stringent integrity requirements defined by civil aviation community, and concentrates on the capability and performance investigation of practical integrity monitoring systems with rigorous mathematical and statistical concepts and approaches. Major contributions of this research are: • Rigorous integrity and continuity risk analysis for aircraft precision approach. Based on the joint probability density function of the affecting components, the integrity and continuity risks of aircraft precision approach with DGPS were computed. This advanced the conventional method of allocating the risk probability. • A theoretical study of RAIM test power. This is the first time a theoretical study on RAIM test power based on the probability statistical theory has been presented, resulting in a new set of RAIM criteria. • Development of a GPS integrity monitoring and DGPS quality control system based on GPS reference station. A prototype of GPS integrity monitoring and DGPS correction prediction system has been developed and tested, based on the A USN A V GPS base station on the roof of QUT ITE Building.

Signal authentication and integrity schemes for next generation global navigation satellite systems

This paper describes a number of techniques for GNSS navigation message authentication. A detailed analysis of the security facilitated by navigation message authentication is given. The analysis takes into consideration the risk of critical applications that rely on GPS including transportation, finance and telecommunication networks. We propose a number of cryptographic authentication schemes for navigation data authentication. These authentication schemes provide authenticity and integrity of the navigation data to the receiver. Through software simulation, the performance of the schemes is quantified. The use of software simulation enables the collection of authentication performance data of different data channels, and the impact of various schemes on the infrastructure and receiver. Navigation message authentication schemes have been simulated at the proposed data rates of Galileo and GPS services, for which the resulting performance data is presented. This paper concludes by making recommendations for optimal implementation of navigation message authentication for Galileo and next generation GPS systems.

Visual assessment of pedestrian crashes

Of the numerous factors that play a role in fatal pedestrian collisions, the time of day, day of the week, and time of year can be significant determinants. More than 60% of all pedestrian collisions in 2007 occurred at night, despite the presumed decrease in both pedestrian and automobile exposure during the night. Although this trend is partially explained by factors such as fatigue and alcohol consumption, prior analysis of the Fatality Analysis Reporting System database suggests that pedestrian fatalities increase as light decreases after controlling for other factors. This study applies graphical cross-tabulation, a novel visual assessment approach, to explore the relationships among collision variables. The results reveal that twilight and the first hour of darkness typically observe the greatest frequency of pedestrian fatal collisions. These hours are not necessarily the most risky on a per mile travelled basis, however, because pedestrian volumes are often still high. Additional analysis is needed to quantify the extent to which pedestrian exposure (walking/crossing activity) in these time periods plays a role in pedestrian crash involvement. Weekly patterns of pedestrian fatal collisions vary by time of year due to the seasonal changes in sunset time. In December, collisions are concentrated around twilight and the first hour of darkness throughout the week while, in June, collisions are most heavily concentrated around twilight and the first hours of darkness on Friday and Saturday. Friday and Saturday nights in June may be the most dangerous times for pedestrians. Knowing when pedestrian risk is highest is critically important for formulating effective mitigation strategies and for efficiently investing safety funds. This applied visual approach is a helpful tool for researchers intending to communicate with policy-makers and to identify relationships that can then be tested with more sophisticated statistical tools.

Attitude observability of a loosely-coupled GPS/Visual Odometry Integrated Navigation Filter

We present a novel method for integrating GPS position estimates with position and attitude estimates derived from visual odometry using a scheme similar to a classic loosely-coupled GPS/INS integration. Under such an arrangement, we derive the error dynamics of the system and develop a Kalman Filter for estimating the errors in position and attitude. Using a control-based approach to observability, we show that the errors in both position and attitude (including yaw) are fully observable when there is a component of acceleration perpendicular to the velocity vector in the navigation frame. Numerical simulations are performed to confirm the observability analysis.

Autonomous aerial navigation and tracking of marine animals

In this paper, we describe the development of an independent and on-board visual servoing system which allows a computationally impoverished aerial vehicle to autonomously identify and track a moving surface target. Our image segmentation and target identification algorithms were developed with the specific task of monitoring whales at sea but could be adapted for other targets. Observing whales is important for many marine biology tasks and is currently performed manually from the shore or from boats. We also present hardware experiments which demonstrate the capabilities of our algorithms for object identification and tracking that enable a flying vehicle to track a moving target.

A two week persistent navigation and mapping experiment using RatSLAM : insights and current developments

One of the major challenges in achieving long term robot autonomy is the need for a SLAM algorithm that can perform SLAM over the operational lifetime of the robot, preferably without human intervention or supervision. In this paper we present insights gained from a two week long persistent SLAM experiment, in which a Pioneer robot performed mock deliveries in a busy office environment. We used the biologically inspired visual SLAM system, RatSLAM, combined with a hybrid control architecture that selected between exploring the environment, performing deliveries, and recharging. The robot performed more than a thousand successful deliveries with only one failure (from which it recovered), travelled more than 40 km over 37 hours of active operation, and recharged autonomously 23 times. We discuss several issues arising from the success (and limitations) of this experiment and two subsequent avenues of work.

Modeling perceived collision risk in port water navigation

An increase in the likelihood of navigational collisions in port waters has put focus on the collision avoidance process in port traffic safety. The most widely used on-board collision avoidance system is the automatic radar plotting aid which is a passive warning system that triggers an alert based on the pilot’s pre-defined indicators of distance and time proximities at the closest point of approaches in encounters with nearby vessels. To better help pilot in decision making in close quarter situations, collision risk should be considered as a continuous monotonic function of the proximities and risk perception should be considered probabilistically. This paper derives an ordered probit regression model to study perceived collision risks. To illustrate the procedure, the risks perceived by Singapore port pilots were obtained to calibrate the regression model. The results demonstrate that a framework based on the probabilistic risk assessment model can be used to give a better understanding of collision risk and to define a more appropriate level of evasive actions.

Towards persistent indoor appearance-based localization, mapping and navigation using CAT-Graph

The challenge of persistent appearance-based navigation and mapping is to develop an autonomous robotic vision system that can simultaneously localize, map and navigate over the lifetime of the robot. However, the computation time and memory requirements of current appearance-based methods typically scale not only with the size of the environment but also with the operation time of the platform; also, repeated revisits to locations will develop multiple competing representations which reduce recall performance. In this paper we present a solution to the persistent localization, mapping and global path planning problem in the context of a delivery robot in an office environment over a one-week period. Using a graphical appearance-based SLAM algorithm, CAT-Graph, we demonstrate constant time and memory loop closure detection with minimal degradation during repeated revisits to locations, along with topological path planning that improves over time without using a global metric representation. We compare the localization performance of CAT-Graph to openFABMAP, an appearance-only SLAM algorithm, and the path planning performance to occupancy-grid based metric SLAM. We discuss the limitations of the algorithm with regard to environment change over time and illustrate how the topological graph representation can be coupled with local movement behaviors for persistent autonomous robot navigation.

Image Region Salience for Improving Appearance-Based Place Recognition using a Supervised Classifier System

Many state of the art vision-based Simultaneous Localisation And Mapping (SLAM) and place recognition systems compute the salience of visual features in their environment. As computing salience can be problematic in radically changing environments new low resolution feature-less systems have been introduced, such as SeqSLAM, all of which consider the whole image. In this paper, we implement a supervised classifier system (UCS) to learn the salience of image regions for place recognition by feature-less systems. SeqSLAM only slightly benefits from the results of training, on the challenging real world Eynsham dataset, as it already appears to filter less useful regions of a panoramic image. However, when recognition is limited to specific image regions performance improves by more than an order of magnitude by utilising the learnt image region saliency. We then investigate whether the region salience generated from the Eynsham dataset generalizes to another car-based dataset using a perspective camera. The results suggest the general applicability of an image region salience mask for optimizing route-based navigation applications.

Why are pedestrian crashes so different in developing countries? A review of relevant factors in relation to their impact in Ethiopia

Much is known about pedestrian behaviour and crash risk in developed countries. In contrast, the literature on pedestrian crash risk in developing countries reveals wide gaps in knowledge and understanding, and a comprehensive assessment is lacking. In particular, pedestrian behaviour in developing countries is fundamentally different in comparison to developed countries, and is influenced by a variety of less well understood contributing factors, leading to difficulty in modelling and predicting pedestrian crash risk and in turn identifying effective safety countermeasures. This paper provides a comprehensive synthesis of the factors known to influence pedestrian crash risk in developing countries, then focuses on Ethiopia as a specific example. The paper identifies where critical gaps in knowledge exist regarding pedestrian crash risk and associated behaviour in developing countries--a set of knowledge gaps which collectively are significant. The paper concludes by articulating a critical research path moving forward, with the aim to achieve an improved understanding of developing country pedestrian crash risk, and an ultimate goal of identifying effective pedestrian safety countermeasures suited to the unique challenges faced by transport system managers in developing countries.