DeepFactors: Real-Time Probabilistic Dense Monocular SLAM

arxiv.org/abs/2001.05049
DeepFactors: Real-Time Probabilistic Dense Monocular SLAM
Jan Czarnowski; Tristan Laidlow; Ronald Clark; Andrew J. Davison
Abstract
The ability to estimate rich geometry and camera motion from monocular imagery is fundamental to future interactive robotics and augmented reality applications. Different approaches have been proposed that vary in scene geometry representation (sparse landmarks, dense maps), the
consistency metric used for optimising the multi-view problem, and the use of learned priors. We present a SLAM system that unifies these methods in a probabilistic framework while still maintaining real-time performance. This is achieved through the use of a learned compact depth
map representation and reformulating three different types of errors: photometric, reprojection and geometric, which we make use of within standard factor graph software. We evaluate our system on trajectory estimation and depth reconstruction on real-world sequences and present
various examples of estimated dense geometry.

[@ArghyaChatterjeeJony]

Great work.

[@xinyangzhao8057]

Very impressive!!!

[@BhargavBardipurkar42]

wow👏👏

[@Abd0z]

Excellent Work, should we expect any public implementation or release for this approach?

[@dysonroboticslaboratoryati9846]

Thank you! We will start working on releasing the code as soon as we finalise the RA-L journal publication.

[@Abd0z]

@@dysonroboticslaboratoryati9846 Great Wish you the best of luck (y)

[@abdelrahmanwaelhelaly1871]

any updates on the code release

[@user-zt8xy4gs4u]

牛逼!