Dense monocular Simultaneous Localization and Mapping by direct surfel optimization

TL;DR

This paper introduces a novel monocular dense SLAM method using direct surfel optimization, representing surfaces as groups of surfels estimated directly from raw pixels, enabling real-time scene reconstruction without traditional regularization.

Contribution

It is the first to apply surfel-based direct optimization for monocular depth estimation, improving accuracy and efficiency by avoiding regularization and initialization routines.

Findings

Achieved real-time scene reconstruction using GPGPU implementation.

Demonstrated accurate depth and normal estimation on multiple datasets.

Showed advantages of physically grounded surfel representation over traditional priors.

Abstract

This work presents a novel approach for monocular dense Simultaneous Localization and Mapping. The surface to be estimated is represented as a piecewise planar surface, defined as a group of surfels each having as parameters its position and normal. These parameters are then directly estimated from the raw camera pixels measurements, by a Gauss-Newton iterative process. As far as the authors know, this is the first time this approach is used for monocular depth estimation. The representation of the surface as a group of surfels has several advantages. It allows the recovery of robust and accurate pixel depths, without the need to use a computationally demanding depth regularization schema. This has the further advantage of avoiding the use of a physically unlikely surface smoothness prior. New surfels can be correctly initialized from the information present in nearby surfels, avoiding also the need to use an expensive initialization routine commonly needed in Gauss-Newton methods. The method was written in the GLSL shading language, allowing the usage of GPGPU thus achieving real-time. The method was tested against several datasets, showing both its depth and normal estimation correctness, and its scene reconstruction quality. The results presented here showcase the usefulness of the more physically grounded piecewise planar scene depth prior, instead of the more commonly pixel depth independence and smoothness prior.