Environmental boundary tracking and estimation using multiple autonomous vehicles

In this paper, we develop a framework for environmental boundary tracking and estimation by considering the boundary as a hidden Markov model (HMM) with separated observations collected from multiple sensing vehicles. For each vehicle, a tracking algorithm is developed based on Page’s cumulative sum algorithm (CUSUM), a method for change-point detection, so that individual vehicles can autonomously track the boundary in a density field with measurement noise. Based on the data collected from sensing vehicles and prior knowledge of the dynamic model of boundary evolvement, we estimate the boundary by solving an optimization problem, in which prediction and current observation are considered in the cost function. Examples and simulation results are presented to verify the efficiency of this approach

Airborne mapping of complex obstacles using 2D Splinegon

This paper describes a recently proposed algorithm in mapping the unknown obstacle in a stationary environment where the obstacles are represented as curved in nature. The focus is to achieve a guaranteed performance of sensor based navigation and mapping. The guaranteed performance is quantified by explicit bounds of the position estimate of an autonomous aerial vehicle using an extended Kalman filter and to track the obstacle so as to extract the map of the obstacle. This Dubins path planning algorithm is used to provide a flyable and safe path to the vehicle to fly from one location to another. This description takes into account the fact that the vehicle is made to fly around the obstacle and hence will map the shape of the obstacle using the 2D-Splinegon technique. This splinegon technique, the most efficient and a robust way to estimate the boundary of a curved nature obstacles, can provide mathematically provable performance guarantees that are achievable in practice

The Filament Sensor for Near Real-Time Detection of Cytoskeletal Fiber Structures

A reliable extraction of filament data from microscopic images is of high interest in the analysis of acto-myosin structures as early morphological markers in mechanically guided differentiation of human mesenchymal stem cells and the understanding of the underlying fiber arrangement processes. In this paper, we propose the filament sensor (FS), a fast and robust processing sequence which detects and records location, orientation, length and width for each single filament of an image, and thus allows for the above described analysis. The extraction of these features has previously not been possible with existing methods. We evaluate the performance of the proposed FS in terms of accuracy and speed in comparison to three existing methods with respect to their limited output. Further, we provide a benchmark dataset of real cell images along with filaments manually marked by a human expert as well as simulated benchmark images. The FS clearly outperforms existing methods in terms of computational runtime and filament extraction accuracy. The implementation of the FS and the benchmark database are available as open source.Comment: 32 pages, 21 figure

Finding Your Way Back: Comparing Path Odometry Algorithms for Assisted Return.

We present a comparative analysis of inertial-based odometry algorithms for the purpose of assisted return. An assisted return system facilitates backtracking of a path previously taken, and can be particularly useful for blind pedestrians. We present a new algorithm for path matching, and test it in simulated assisted return tasks with data from WeAllWalk, the only existing data set with inertial data recorded from blind walkers. We consider two odometry systems, one based on deep learning (RoNIN), and the second based on robust turn detection and step counting. Our results show that the best path matching results are obtained using the turns/steps odometry system

Progressive surface modeling scheme from unorganised curves

This paper presents a novel surface modelling scheme to construct a freeform surface progressively from unorganised curves representing the boundary and interior characteristic curves. The approach can construct a base surface model from four ordinary or composite boundary curves and support incremental surface updating from interior characteristic curves, some of which may not be on the final surface. The base surface is first constructed as a regular Coons surface and upon receiving an interior curve sketch, it is then updated. With this progressive modelling scheme, a final surface with multiple sub-surfaces can be obtained from a set of unorganised curves and transferred to commercial surface modelling software for detailed modification. The approach has been tested with examples based on 3D motion sketches; it is capable of dealing with unorganised design curves for surface modelling in conceptual design. Its limitations have been discussed

A disaster risk assessment model for the conservation of cultural heritage sites in Melaka Malaysia

There exist ongoing efforts to reduce the exposure of Cultural Heritage Sites (CHSs) to Disaster Risk (DR). However, a complicated issue these efforts face is that of ‘estimation’ whereby no standardised unit exist for assessing the effects of Cultural Heritage (CH) exposed to DR as compared to other exposed items having standardised assessment units such as; ‘number of people’ for deaths, injured and displaced, ‘dollar’ for economic impact, ‘number of units’ for building stock or animals among others. This issue inhibits the effective assessment of CHSs exposed to DR. Although there exist several DR assessment frameworks for conserving CHSs, the conceptualisation of DR in these studies fall short of good practice such as international strategy for disaster reduction by United Nations which expresses DR to being a hollistic interplay of three variables (hazard, vulnerability and capacity). Adopting such good practice, this research seeks to propose a mechanism of DR assessment aimed at reducing the exposure of CHSs to DR. Quantitative method adopted for data collection involved a survey of 365 respondents at CHSs in Melaka using a structured questionnaire. Similarly, data analysis consisted of a two-step Structural Equation Modelling (measurement and structural modelling). The achievement of the recommended thresholds for unidimensionality, validity and reliability by the measurement models is a testimony to the model fitness for all 8 first-order independent variables and 2 first-order dependent variables. While hazard had a ‘small’ but negative effect, vulnerability had a ‘very large’ but negative effect on the exposure of CHSs to DR. Likewise, capacity had a ‘small’ but positive effect on the exposure of CHSs to DR. The outcome of this study is a Disaster Risk Assessment Model (DRAM) aimed at reducing DR to CHSs. The implication of this research is providing insights on decisions for DR assessment to institutions, policymakers and statutory bodies towards their approach to enhancing the conservation of CHSs

Arcfinder: An algorithm for the automatic detection of gravitational arcs

We present an efficient algorithm designed for and capable of detecting elongated, thin features such as lines and curves in astronomical images, and its application to the automatic detection of gravitational arcs. The algorithm is sufficiently robust to detect such features even if their surface brightness is near the pixel noise in the image, yet the amount of spurious detections is low. The algorithm subdivides the image into a grid of overlapping cells which are iteratively shifted towards a local centre of brightness in their immediate neighbourhood. It then computes the ellipticity for each cell, and combines cells with correlated ellipticities into objects. These are combined to graphs in a next step, which are then further processed to determine properties of the detected objects. We demonstrate the operation and the efficiency of the algorithm applying it to HST images of galaxy clusters known to contain gravitational arcs. The algorithm completes the analysis of an image with 3000x3000 pixels in about 4 seconds on an ordinary desktop PC. We discuss further applications, the method's remaining problems and possible approaches to their solution.Comment: 12 pages, 12 figure

Model-based estimation of off-highway road geometry using single-axis LADAR and inertial sensing

This paper applies some previously studied extended Kalman filter techniques for planar road geometry estimation to the domain of autonomous navigation of off-highway vehicles. In this work, a clothoid model of the road geometry is constructed and estimated recursively based on road features extracted from single-axis LADAR range measurements. We present a method for feature extraction of the road centerline in the image plane, and describe its application to recursive estimation of the road geometry. We analyze the performance of our method against simulated motion of varied road geometries and against closed-loop detection, tracking and following of desert roads. Our method accomodates full 6 DOF motion of the vehicle as it navigates, constructs consistent estimates of the road geometry with respect to a fixed global reference frame, and requires an estimate of the sensor pose for each range measurement