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

Ground robots always get collision in that only if they get close to theobstacles, can they sense the danger and take actions, which is usually toolate to avoid the crash, causing severe damage to the robots. To address thisissue, we present collaboration of aerial and ground robots in recognition offeasible region. Taking the aerial robots' advantages of having large scalevariance of view points of the same route which the ground robots is on, thecollaboration work provides global information of road segmentation for theground robot, thus enabling it to obtain feasible region and adjust its poseahead of time. Under normal circumstance, the transformation between these twodevices can be obtained by GPS yet with much error, directly causing inferiorinfluence on recognition of feasible region. Thereby, we utilize thestate-of-the-art research achievements in matching heterogeneous sensormeasurements called deep phase correlation network(DPCN), which has excellentperformance on heterogeneous mapping, to refine the transformation. The networkis light-weighted and promising for better generalization. We use Aero-Grounddataset which consists of heterogeneous sensor images and aerial roadsegmentation images. The results show that our collaborative system has greataccuracy, speed and stability.

Collaborative Recognition of Feasible Region with Aerial and Ground Robots through DPCN