Environment representation for mobile robot localisation

Ranasinghe, Ravindra; Dissanayake, Gamini; Furukawa, Tomonari; Arukgoda, Janindu; Dantanarayana, Lakshitha

doi:10.1109/iciinfs.2017.8300384

Cited by 3 publications

(5 citation statements)

References 13 publications

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“…The geometry of the environment is implicitly captured in the DF as a level set of a function defined over the space in which the geometry is defined. Therefore, in addition to capturing the occupied regions of the environment, by providing a continuous measure of the distance to the closets occupied region, it offers a much richer representation in comparison to occupancy grid maps [6]. There are several types of distance functions.…”

Section: A Environment Representation With Distance Functionsmentioning

confidence: 99%

“…A similar equation can be derived in the case where the environment is to be represented by its SDF. It is important to note that the VDF and the gradient of the SDF are both discontinuous at the cut locus [6]. Therefore, the cubic spline approximation is not able to represent the distance function in this region and as a consequence Eikonal equation can not be enforced.…”

Section: B Parametric Approximation Of a Solution To A Pdementioning

confidence: 99%

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Representation of Uncertain Occupancy Maps with High Level Feature Vectors

Arukgoda

Ranasinghe

Dissanayake

2019

2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)

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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.

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Section: A Environment Representation With Distance Functionsmentioning

confidence: 99%

Section: B Parametric Approximation Of a Solution To A Pdementioning

confidence: 99%

Representation of Uncertain Occupancy Maps with High Level Feature Vectors

Arukgoda

Ranasinghe

Dissanayake

2019

2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)

Self Cite

View full text Add to dashboard Cite

show abstract

“…When the width of occupied regions of the environment are similar in size to the resolution of the sensors, for example, in the case of an indoor environment with thin walls, use of the unsigned distance function is more convenient. In this form of DF, the absolute value of the distance from a point to the nearest boundary is assigned to the DF [6]. When V is the set of occupied grid cells in an occupancy grid map, the DF can be expressed by (1) at any given point x zi .…”

Section: A Distance Function Mapsmentioning

confidence: 99%

“…Therefore, these maps not only encode the occupied regions of the environment but also provide a continuous measure of the distance, a much richer representation of the environment. In [5], [6], [7] authors have demonstrated the use of different formulations of distance functions for localising robots in both indoor and outdoor environments.…”

Section: Introductionmentioning

confidence: 99%