English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We consider how to directly extract a road map (also known as a topologicalrepresentation) of an initially-unknown 2-dimensional environment via an onlineprocedure that robustly computes a retraction of its boundaries. In thisarticle, we first present the online construction of a topological map and theimplementation of a control law for guiding the robot to the nearest unexploredarea, first presented in [1]. The proposed method operates by allowing therobot to localize itself on a partially constructed map, calculate a path tounexplored parts of the environment (frontiers), compute a robust terminatingcondition when the robot has fully explored the environment, and achieve loopclosure detection. The proposed algorithm results in smooth safe paths for therobot's navigation needs. The presented approach is any time algorithm that hasthe advantage that it allows for the active creation of topological maps fromlaser scan data, as it is being acquired. We also propose a navigation strategybased on a heuristic where the robot is directed towards nodes in thetopological map that open to empty space. We then extend the work in [1] bypresenting a topology matching algorithm that leverages the strengths of aparticular spectral correspondence method [2], to match the mapped environmentsgenerated from our topology-making algorithm. Here, we concentrated onimplementing a system that could be used to match the topologies of the mappedenvironment by using AOF Skeletons. In topology matching between two given mapsand their AOF skeletons, we first find correspondences between points on theAOF skeletons of two different environments. We then align the (2D) points ofthe environments themselves. We also compute a distance measure between twogiven environments, based on their extracted AOF skeletons and their topology,as the sum of the matching errors between corresponding points.

Average Outward Flux Skeletons for Environment Mapping and Topology Matching