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

Obstacle avoidance from monocular images is a challenging problem for robots.Though multi-view structure-from-motion could build 3D maps, it is not robustin textureless environments. Some learning based methods exploit humandemonstration to predict a steering command directly from a single image.However, this method is usually biased towards certain tasks or demonstrationscenarios and also biased by human understanding. In this paper, we propose anew method to predict a trajectory from images. We train our system on morediverse NYUv2 dataset. The ground truth trajectory is computed from thedesigned cost functions automatically. The Convolutional Neural Networkperception is divided into two stages: first, predict depth map and surfacenormal from RGB images, which are two important geometric properties related to3D obstacle representation. Second, predict the trajectory from the depth andnormal. Results show that our intermediate perception increases the accuracy by20% than the direct prediction. Our model generalizes well to other publicindoor datasets and is also demonstrated for robot flights in simulation andexperiments.

Obstacle Avoidance through Deep Networks based Intermediate Perception