Naslov
MOFT: Monocular odometry based on deep depth and careful feature selection and tracking

Autori
Koledić, Karlo ; Cvišić, Igor ; Marković, Ivan ; Petrović, Ivan

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
IEEE International Conference on Robotics and Automation (ICRA) 2023 / - , 2023, 1-6

Skup
IEEE International Conference on Robotics and Automation (ICRA) 2023

Mjesto i datum
London, United Kingdom, 29.05.2023. - 02.06.2023

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
visual odometry ; deep learning ; SLAM

Sažetak
Autonomous localization in unknown environ- ments is a fundamental problem in many emerging fields and the monocular visual approach offers many advantages, due to being a rich source of information and avoiding comparatively more complicated setups and multisensor calibration. Deep learning opened new venues for monocular odometry yielding not only end-to-end approaches but also hybrid methods com- bining the well studied geometry with specific deep components. In this paper we propose a monocular odometry that leverages deep depth within a feature based geometrical framework yielding a lightweight frame-to-frame approach with metrically scaled trajectories and state-of-the-art accuracy. The front- end is based on a multihypothesis matcher with perspective correction coupled with deep depth predictions that enables careful feature selection and tracking ; especially of ground plane features that are suitable for translation estimation. The back-end is based on point-to-epipolar line minimization for rotation and unit translation estimation, followed by deep depth aided reprojection error minimization for metrically correct translation estimation. Furthermore, we also present a domain shift adaptation approach that allows for generalization over different camera intrinsic and extrinsic setups. The proposed approach is evaluated on the KITTI and KITTI-360 datasets, showing competitive results and in most cases outperforming other state-of-the-art stereo and monocular methods.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Temeljne tehničke znanosti

POVEZANOST RADA