Towards Generation and Evaluation of Comprehensive Mapping Robot Datasets
This provides a reproducible benchmark for evaluating SLAM algorithms in robotics, though it is incremental as it focuses on dataset generation rather than new algorithmic advances.
The paper introduces a hardware-synchronized mapping robot with external tracking and a 3D scanner to generate three datasets for evaluating mapping algorithms in indoor environments, producing maps and trajectory data for reproducible comparisons and drawing conclusions about SLAM algorithm performance.
This paper presents a fully hardware synchronized mapping robot with support for a hardware synchronized external tracking system, for super-precise timing and localization. We also employ a professional, static 3D scanner for ground truth map collection. Three datasets are generated to evaluate the performance of mapping algorithms within a room and between rooms. Based on these datasets we generate maps and trajectory data, which is then fed into evaluation algorithms. The mapping and evaluation procedures are made in a very easily reproducible manner for maximum comparability. In the end we can draw a couple of conclusions about the tested SLAM algorithms.