Localisation, Mapping, SLAM
Hodges, J, Attia, T, Arukgoda, J, Kang, C, Cowden, M, Doan, L, Ranasinghe, R, Abdelatty, K, Dissanayake, G & Furukawa, T 2019, 'Multistage bayesian autonomy for high-precision operation in a large field', Journal of Field Robotics, vol. 36, no. 1, pp. 183-203.View/Download from: Publisher's site
© 2018 Wiley Periodicals, Inc. This paper presents a generalized multistage bayesian framework to enable an autonomous robot to complete high-precision operations on a static target in a large field. The proposed framework consists of two multistage approaches, capable of dealing with the complexity of high-precision operation in a large field to detect and localize the target. In the multistage localization, locations of the robot and the target are estimated sequentially when the target is far away from the robot, whereas these locations are estimated simultaneously when the target is close. A level of confidence (LOC) for each detection criterion of a sensor and the associated probability of detection (POD) of the sensor are defined to make the target detectable with different LOCs at varying distances. Differential entropies of the robot and target are used as a precision metric for evaluating the performance of the proposed approach. The proposed multistage observation and localization approaches were applied to scenarios using an unmanned ground vehicle (UGV) and an unmanned aerial vehicle (UAV). Results with the UGV in simulated environments and then real environments show the effectiveness of the proposed approaches to real-world problems. A successful demonstration using the UAV is also presented.
Arukgoda, J, Ranasinghe, R & Dissanayake, G 2019, 'Representation of uncertain occupancy maps with high level feature vectors', IEEE International Conference on Automation Science and Engineering, pp. 1035-1041.View/Download from: Publisher's site
© 2019 IEEE. This paper presents a novel method for representing an uncertain occupancy map using a 'feature vector' and an associated covariance matrix. Input required is a point cloud generated using observations from a sensor captured at different locations in the environment. Both the sensor locations and the measurements themselves may have an associated uncertainty. The output is a set of coefficients and their uncertainties of a cubic spline approximation to the distance function of the environment, thereby resulting in a compact parametric representation of the environment geometry. Cubic spline coefficients are computed by solving a non-linear least squares problem that enforces the Eikonal equation over the space in which the environment geometry is defined, and zero boundary condition at each observation in the point cloud. It is argued that a feature based representation of point cloud maps acquired from uncertain locations using noisy sensors has the potential to open up a new direction in robot mapping, localisation and SLAM. Numerical examples are presented to illustrate the proposed technique.
Arukgoda, J, Ranasinghe, R & Dissanayake, G 2019, 'Robot localisation in 3D environments using sparse range measurements', IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM, pp. 551-558.View/Download from: Publisher's site
© 2019 IEEE. This paper presents an algorithm for mobile robot localisation given a map of a 3D environment and a sparse set of range-bearing measurements. The environment is represented using a spline approximation of its vector distance function (VDF). For a given location in the environment, VDF encodes the distance to the nearest occupied region along three orthogonal axes. VDF is first obtained from an occupancy voxel map and its three components are then approximated in the least-square sense using a set of three dimensional cubic b-splines, providing a rich and continuous representation of the environment. First and second order derivatives of the VDF are also computed and stored. The difference between an observed range measurement in a given direction and its expected value is formulated as a function of the robot location and the spline coefficients representing the VDF. This leads to a non-linear least-squares optimization problem that can be solved to localise the robot given a set of such measurements. It is demonstrated that a sparse set of range-bearing measurements, an order of magnitude smaller than what is typically available from 3D range sensor is adequate to achieve accurate localisation. The algorithm presented is illustrated using a number of examples including a single point range sensor mounted on a pan-tilt head to localise a robot moving in an indoor environment.
Arukgoda, J, Ranasinghe, R, Dantanarayana, L, Dissanayake, G & Furukawa, T 2017, 'Vector Distance Function Based Map Representation for Robot Localisation', Australasian Conference on Robotics and Automation, Australasian Conference on Robotics and Automation, ACRA, Sydney Australia, pp. 1-8.
This paper introduces the use of the vector
distance function (VDF) for representing environments,
particularly for the use in localisation
algorithms. It is shown that VDF has
a continuous derivative at the object boundary
in contrast to unsigned distance transform,
and does not require an environment populated
with closed object as in the case of the
signed distance transforms, the two most common
strategies reported in the literature for
representing environments based on distances
to nearest occupied regions. As such VDF overcomes
the main disadvantages of the existing
distance transform based representations in the
context of robot localisation. The key properties
of VDF are demonstrated and the use of
VDF in robot localisation using an optimization
based algorithm is illustrated using three
examples. It is shown that the proposed environment
representation and the localisation
algorithm is effective in providing accurate location
estimates as well as the associated uncertainties
Arukgoda, J, Ranasinghe, R, Danthanarayana, L, Dissanayake, G & Furukawa, T 2017, 'Vector distance function based map representation for robot localisation', Australasian Conference on Robotics and Automation, ACRA, pp. 165-172.
This paper introduces the use of the vector distance function (VDF) for representing environments, particularly for the use in localisation algorithms. It is shown that VDF has a continuous derivative at the object boundary in contrast to unsigned distance transform, and does not require an environment populated with closed object as in the case of the signed distance transforms, the two most common strategies reported in the literature for representing environments based on distances to nearest occupied regions. As such VDF overcomes the main disadvantages of the existing distance transform based representations in the context of robot localisation. The key properties of VDF are demonstrated and the use of VDF in robot localisation using an optimization based algorithm is illustrated using three examples. It is shown that the proposed environment representation and the localisation algorithm is effective in providing accurate location estimates as well as the associated uncertainties.
Perera, A, Arukgoda, J, Ranasinghe, RS & Dissanayake, G 2017, 'Localization System for Carers to Track Elderly People in Visits to a Crowded Shopping Mall', 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), International Conference on Indoor Positioning and Indoor Navigation, IEEE, Sapporo, Japan, pp. 1-8.View/Download from: Publisher's site
This work presents a real-time localization system
developed for professional care givers to track residents of an
aged care facility during their visits to a crowded, multi-story
shopping mall. The proposed system consists of a Wi-Fi based
self-localization platform integrated into a wheeled walking frame
and an application installed in a hand-held tablet device for
displaying the locations of walker users. The density of people in
the shopping mall changes significantly during the day thus the
expected Wi-Fi signal strength at a given location is subject to
large variations. However, Identifying the location to be within
a given area is adequate and the average speed of motion is
less than 0.5 m/sec. In this paper, an algorithm that addresses
these unique requirements is presented. We exploit the signal
strength characteristics of existing Wi-Fi network and prior
knowledge of the building floor plans for developing our core
algorithm. The environments is divided in to cells that are
either enclosed spaces or divisions of larger open regions. The
probability density function of the Wi-Fi signal strength of each
cell is estimated using Kernel Density Estimation (KDE) and is
used in a probabilistic framework to estimate the user location.
Motion model of the users as well as the detection of floor
transition events are used to enhance the performance of the
location estimator. The algorithm was implemented using an
Odroid-C1 computer and a tablet with Android operating system.
Results obtained during field trials at Roselands Shopping Mall
in Sydney are presented.
Ranasinghe, R, Dissanayake, G, Furukawa, T, Arukgoda, J & Dantanarayana, L 2017, 'Environment Representation for Mobile Robot Localisation (Invited Paper)', 2017 IEEE International Conference on Industrial and Information Systems (ICIIS), IEEE International Conference on Industrial and Information Systems, IEEE, Peradeniya, Sri Lanka, pp. 296-301.View/Download from: Publisher's site
An adequate representation of the environment is an essential component of a mobile robot navigation system. This paper reviews the techniques reported in the literature for capturing the geometry of the space surrounding a mobile robot. In particular, the use of distance functions that combine some of the advantages of feature based and occupancy grid based representations for mobile robot localisation is described in detail. The effectiveness of various distance function based representations is demonstrated using a number of practical examples for localising ground and air vehicles.
Unicomb, J, Dantanarayana, L, Arukgoda, J, Ranasinghe, R, Dissanayake, G & Furukawa, T 2017, 'Distance function based 6DOF localization for unmanned aerial vehicles in GPS denied environments', 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Vancouver, BC, Canada.View/Download from: Publisher's site
This paper presents an algorithm for localizing an unmanned aerial vehicle (UAV) in GPS denied environments. Localization is performed with respect to a pre-built map of the environment represented using the distance function of a binary mosaic, avoiding the need for extraction and explicit matching of visual features. Edges extracted from images acquired by an on-board camera are projected to the map to compute an error metric that indicates the misalignment between the predicted and true pose of the UAV. A constrained extended Kalman filter (EKF) framework is used to generate an estimate of the full 6-DOF location of the UAV by enforcing the condition that the distance function values are zero when there is no misalignment. Use of an EKF also makes it possible to seamlessly incorporate information from any other system on the UAV, for example, from its auto-pilot, a height sensor or an optical flow sensor. Experiments using a hexarotor UAV both in a simulation environment and in the field are presented to demonstrate the effectiveness of the proposed algorithm.