Real-time CPU-based large-scale 3D mesh reconstruction

TL;DR

This paper presents a novel CPU-based incremental 3D mesh reconstruction algorithm that operates in real-time, effectively updating the mesh with SLAM data, and outperforms existing methods in speed while maintaining accuracy.

Contribution

It introduces the first real-time incremental manifold mesh algorithm for CPU that updates with SLAM points, extending prior methods to large-scale environments.

Findings

Achieves real-time performance on CPU for large-scale 3D mesh reconstruction.

Maintains comparable accuracy to state-of-the-art methods.

Outperforms existing algorithms in speed by an order of magnitude.

Abstract

In Robotics, especially in this era of autonomous driving, mapping is one key ability of a robot to be able to navigate through an environment, localize on it and analyze its traversability. To allow for real-time execution on constrained hardware, the map usually estimated by feature-based or semi-dense SLAM algorithms is a sparse point cloud; a richer and more complete representation of the environment is desirable. Existing dense mapping algorithms require extensive use of GPU computing and they hardly scale to large environments; incremental algorithms from sparse points still represent an effective solution when light computational effort is needed and big sequences have to be processed in real-time. In this paper we improved and extended the state of the art incremental manifold mesh algorithm proposed in [1] and extended in [2]. While these algorithms do not achieve real-time and they embed points from SLAM or Structure from Motion only when their position is fixed, in this paper we propose the first incremental algorithm able to reconstruct a manifold mesh in real-time through single core CPU processing which is also able to modify the mesh according to 3D points updates from the underlying SLAM algorithm. We tested our algorithm against two state of the art incremental mesh mapping systems on the KITTI dataset, and we showed that, while accuracy is comparable, our approach is able to reach real-time performances thanks to an order of magnitude speed-up.