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.

One-stop government portals : transformation or navigation?

E-government is seen as a promising approach for governments to improve their service towards citizens and become more cost-efficient in service delivery. This is often combined with one-stop government, which is a citizen-oriented approach stressing integrated provision of services from multiple departments via a single access point, the one-stop government portal. While the portal concept is gaining prominence in practice, there is little know about its status in academic literature. This hinders academics in building an accumulated body of knowledge around the concept and makes it hard for practitioners to access relevant academic insights on the topic. The objective of this study is to identify and understand the key themes of the one-stop government portal concept in academic, e-government research. A holistic analysis is provided by addressing different viewpoints: social-political, legal, organizational, user, security, service, data & information, and technical. As overall finding we conclude that there are two different approaches: a more pragmatic approach focuses on quick wins in particular related to usability and navigation and a more ambitious, transformational approach having far reaching social-political, legal, organizational implications.

Image-based visual navigation for mobile robots

We introduce a new image-based visual navigation algorithm that allows the Cartesian velocity of a robot to be defined with respect to a set of visually observed features corresponding to previously unseen and unmapped world points. The technique is well suited to mobile robot tasks such as moving along a road or flying over the ground. We describe the algorithm in general form and present detailed simulation results for an aerial robot scenario using a spherical camera and a wide angle perspective camera, and present experimental results for a mobile ground robot.

Vision-only autonomous navigation using topometric maps

This paper presents a mapping and navigation system for a mobile robot, which uses vision as its sole sensor modality. The system enables the robot to navigate autonomously, plan paths and avoid obstacles using a vision based topometric map of its environment. The map consists of a globally-consistent pose-graph with a local 3D point cloud attached to each of its nodes. These point clouds are used for direction independent loop closure and to dynamically generate 2D metric maps for locally optimal path planning. Using this locally semi-continuous metric space, the robot performs shortest path planning instead of following the nodes of the graph --- as is done with most other vision-only navigation approaches. The system exploits the local accuracy of visual odometry in creating local metric maps, and uses pose graph SLAM, visual appearance-based place recognition and point clouds registration to create the topometric map. The ability of the framework to sustain vision-only navigation is validated experimentally, and the system is provided as open-source software.

The use of navigation to achieve soft tissue balance in total knee arthroplasty : a randomised clinical study

Background: Achieving soft tissue balance is an operative goal in total knee arthroplasty. This randomised, prospective study compared computer navigation to conventional techniques in achieving soft tissue balance. Methods: Forty one consecutive knee arthroplasties were randomised to either a non-navigated or navigated group. In the non-navigated group, balancing was carried out using surgeon judgement. In the navigated group, balancing was carried out using navigation software. In both groups, the navigation software was used as a measuring tool. Results: Balancing of the mediolateral extension gap was superior in the navigation group (p=0.001). No significant difference was found between the two groups in balancing the mediolateral flexion gap or in achieving equal flexion and extension gaps. Conclusions: Computer navigation offered little advantage over experienced surgeon judgement in achieving soft tissue balance in knee replacement. However, the method employed in the navigated group did provide a reproducible and objective assessment of flexion and extension gaps and may therefore benefit surgeons in training.

Multi-level information fusion for environment aware robotic navigation

This thesis develops the hardware and software framework for an integrated navigation system. Dynamic data fusion algorithms are used to develop a system with a high level of resistance to the typical problems that affect standard navigation systems.

Monocular vision based autonomous navigation for a cost-effective MAV in GPS-denied environments

In this paper, we present a monocular vision based autonomous navigation system for Micro Aerial Vehicles (MAVs) in GPS-denied environments. The major drawback of monocular systems is that the depth scale of the scene can not be determined without prior knowledge or other sensors. To address this problem, we minimize a cost function consisting of a drift-free altitude measurement and up-to-scale position estimate obtained using the visual sensor. We evaluate the scale estimator, state estimator and controller performance by comparing with ground truth data acquired using a motion capture system. All resources including source code, tutorial documentation and system models are available online.

A recommendation for designing mobile pedestrian navigation system in university campuses

University campuses have thousands of new students, staff and visitors every year. For those who are unfamiliar with the campus environment, an effective pedestrian navigation system is essential to orientate and guide them around the campus. Compared to traditional navigation systems, such as physical signposts and digital map kiosks, a mobile pedestrian navigation system provides advantages in terms of mobility, sensing capabilities, weather-awareness when the user is on the go. However, how best to design a mobile pedestrian navigation system for university campuses is still vague due to limited research in understanding how pedestrians interact with the system, and what information is required for traveling in a complex environment such as university campus. In this paper, we present a mobile pedestrian navigation system called QUT Nav. A field study with eight participants was run in a university campus context, aiming to identify key information required in a mobile pedestrian navigation system for user traveling in university campuses. It also investigated user's interactions and behaviours while they were navigating in the campus environment. Based on the results from the field study, a recommendation for designing mobile pedestrian navigation systems for university campuses is stated.

Passenger familiarity and intuitive navigation within airport environments

Passengers navigating through airports can experience confusion or become lost, resulting in dissatisfaction, missed flights and flight delays. Passengers moving through airports are required to make many navigation decisions, for example to find the correct check-in desk or find the correct boarding gate. Prior experience of using the airports is likely to enable intuitive navigation, however limited research on this topic currently exists. In this paper we investigate passenger navigation by observing 30 participants at one international airport as they moved from check-in to a departure gate. The results indicate that passengers do spend time navigating intuitively through the airport, and that there is a positive correlation between intuitive navigation and airport familiarity. It was also found that participants with lower airport familiarity spend a greater percentage of overall navigation time searching and assessing/acquiring information than high familiarity participants. These findings provide evidence that passengers with higher airport familiarity have a greater understanding of the process, have a better understanding of what information to look for and use this familiarity to navigate intuitively. Findings from this research will have design implications for both current, and future airport terminals and other large spaces that people navigate through.

Towards persistent visual navigation using SMART

In this paper, we present SMART (Sequence Matching Across Route Traversals): a vision- based place recognition system that uses whole image matching techniques and odometry information to improve the precision-recall performance, latency and general applicability of the SeqSLAM algorithm. We evaluate the system’s performance on challenging day and night journeys over several kilometres at widely varying vehicle velocities from 0 to 60 km/h, compare performance to the current state-of- the-art SeqSLAM algorithm, and provide parameter studies that evaluate the effectiveness of each system component. Using 30-metre sequences, SMART achieves place recognition performance of 81% recall at 100% precision, outperforming SeqSLAM, and is robust to significant degradations in odometry.

Eight weeks of episodic visual navigation inside a non-stationary environment using adaptive spherical views

This paper presents a long-term experiment where a mobile robot uses adaptive spherical views to localize itself and navigate inside a non-stationary office environment. The office contains seven members of staff and experiences a continuous change in its appearance over time due to their daily activities. The experiment runs as an episodic navigation task in the office over a period of eight weeks. The spherical views are stored in the nodes of a pose graph and they are updated in response to the changes in the environment. The updating mechanism is inspired by the concepts of long- and short-term memories. The experimental evaluation is done using three performance metrics which evaluate the quality of both the adaptive spherical views and the navigation over time.

Hearing loss and a supportive tactile signal in a navigation system : effects on driving behavior and eye movements

An on-road study was conducted to evaluate a complementary tactile navigation signal on driving behaviour and eye movements for drivers with hearing loss (HL) compared to drivers with normal hearing (NH). 32 participants (16 HL and 16 NH) performed two preprogrammed navigation tasks. In one, participants received only visual information, while the other also included a vibration in the seat to guide them in the correct direction. SMI glasses were used for eye tracking, recording the point of gaze within the scene. Analysis was performed on predefined regions. A questionnaire examined participant's experience of the navigation systems. Hearing loss was associated with lower speed, higher satisfaction with the tactile signal and more glances in the rear view mirror. Additionally, tactile support led to less time spent viewing the navigation display.

Designing mobile systems for social navigation in urban public places

This thesis presents social requirements and design considerations from a study evaluating interactive approaches to social navigation and user-generated information sharing in urban environments using mobile devices. It investigates innovative ways to leverage mobile information and communication technology in order to provide a social navigation platform for residents and visitors in and for public urban places. Through a design case study this work presents CityFlocks, a mobile information system that offers an easy way for information-seeking new residents or visitors to access tacit knowledge from local people about their new community. It is intended to enable visitors and new residents in a city to tap into the knowledge and experiences of local residents in order to gather information about their new environment. Its design specifically aims to lower existing barriers of access and facilitate social navigation in urban places. In various user tests it evaluates two general user interaction alternatives – direct and indirect social navigation – and analyses which interaction method works better for people using a mobile device to socially navigate urban environments. The outcomes are relevant for the user interaction design of future mobile information systems that leverage the social navigation approach.

RatSLAM : using models of rodent hippocampus for robot navigation and beyond

We describe recent biologically-inspired mapping research incorporating brain-based multi-sensor fusion and calibration processes and a new multi-scale, homogeneous mapping framework. We also review the interdisciplinary approach to the development of the RatSLAM robot mapping and navigation system over the past decade and discuss the insights gained from combining pragmatic modelling of biological processes with attempts to close the loop back to biology. Our aim is to encourage the pursuit of truly interdisciplinary approaches to robotics research by providing successful case studies.

Resilient Navigation through Probabilistic Modality Reconfiguration

This paper proposes an approach to achieve resilient navigation for indoor mobile robots. Resilient navigation seeks to mitigate the impact of control, localisation, or map errors on the safety of the platform while enforcing the robot’s ability to achieve its goal. We show that resilience to unpredictable errors can be achieved by combining the benefits of independent and complementary algorithmic approaches to navigation, or modalities, each tuned to a particular type of environment or situation. In this paper, the modalities comprise a path planning method and a reactive motion strategy. While the robot navigates, a Hidden Markov Model continually estimates the most appropriate modality based on two types of information: context (information known a priori) and monitoring (evaluating unpredictable aspects of the current situation). The robot then uses the recommended modality, switching between one and another dynamically. Experimental validation with a SegwayRMP- based platform in an office environment shows that our approach enables failure mitigation while maintaining the safety of the platform. The robot is shown to reach its goal in the presence of: 1) unpredicted control errors, 2) unexpected map errors and 3) a large injected localisation fault.