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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

For two decades, ego-motion estimation is an actively developing topic in computer vision and robotics. The principle of existing motion estimation techniques relies on the minimisation of an energy function based on re-projection errors. In this paper we augment such an energy function by introducing an epipolar-geometryderived regularisation term. The experiments prove that, by taking soft constraints into account, a more reliable motion estimation is achieved. It also shows that the implementation presented in this paper is able to achieve a remarkable accuracy comparative to the stereo vision approaches, with an overall drift maintained under 2% over hundreds of metres.

Regularised Energy Model for Robust Monocular Ego-motion Estimation