Efficient Continuous-Time SLAM for 3D Lidar-Based Online Mapping

TL;DR

This paper introduces an efficient 3D SLAM method that refines scan alignments during online mapping using hierarchical optimization, local multiresolution maps, and continuous-time trajectory modeling to improve accuracy in high-data-rate scenarios.

Contribution

It presents a novel 3D SLAM approach combining local mapping, hierarchical graph optimization, and continuous-time trajectory modeling for improved online mapping accuracy.

Findings

Effective local multiresolution mapping with surfel registration

Hierarchical graph optimization improves map consistency

Quantitative entropy-based measure evaluates map quality

Abstract

Modern 3D laser-range scanners have a high data rate, making online simultaneous localization and mapping (SLAM) computationally challenging. Recursive state estimation techniques are efficient but commit to a state estimate immediately after a new scan is made, which may lead to misalignments of measurements. We present a 3D SLAM approach that allows for refining alignments during online mapping. Our method is based on efficient local mapping and a hierarchical optimization back-end. Measurements of a 3D laser scanner are aggregated in local multiresolution maps by means of surfel-based registration. The local maps are used in a multi-level graph for allocentric mapping and localization. In order to incorporate corrections when refining the alignment, the individual 3D scans in the local map are modeled as a sub-graph and graph optimization is performed to account for drift and misalignments in the local maps. Furthermore, in each sub-graph, a continuous-time representation of the sensor trajectory allows to correct measurements between scan poses. We evaluate our approach in multiple experiments by showing qualitative results. Furthermore, we quantify the map quality by an entropy-based measure.