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

Human detection and tracking is an essential task for service robots, wherethe combined use of multiple sensors has potential advantages that are yet tobe exploited. In this paper, we introduce a framework allowing a robot to learna new 3D LiDAR-based human classifier from other sensors over time, takingadvantage of a multisensor tracking system. The main innovation is the use ofdifferent detectors for existing sensors (i.e. RGB-D camera, 2D LiDAR) totrain, online, a new 3D LiDAR-based human classifier, exploiting a so-calledtrajectory probability. Our framework uses this probability to check whethernew detections belongs to a human trajectory, estimated by different sensorsand/or detectors, and to learn a human classifier in a semi-supervised fashion.The framework has been implemented and tested on a real-world dataset collectedby a mobile robot. We present experiments illustrating that our system is ableto effectively learn from different sensors and from the environment, and thatthe performance of the 3D LiDAR-based human classification improves with thenumber of sensors/detectors used.

Multisensor Online Transfer Learning for 3D LiDAR-based Human Detection with a Mobile Robot