RTS-GT: Robotic Total Stations Ground Truthing dataset

TL;DR

The RTS-GT dataset offers a comprehensive, high-precision ground truth for SLAM evaluation using robotic total stations, outperforming GNSS in stability across diverse environments.

Contribution

This paper introduces the RTS-GT dataset, the first extensive collection of 6-DOF ground truth trajectories generated by RTSs, with detailed pose precision data for SLAM benchmarking.

Findings

RTS measurements are 22 times more stable than GNSS.

The dataset includes over 49 km of trajectories from 60 experiments.

RTS-GT is the most extensive RTS-based dataset to date.

Abstract

Numerous datasets and benchmarks exist to assess and compare Simultaneous Localization and Mapping (SLAM) algorithms. Nevertheless, their precision must follow the rate at which SLAM algorithms improved in recent years. Moreover, current datasets fall short of comprehensive data-collection protocol for reproducibility and the evaluation of the precision or accuracy of the recorded trajectories. With this objective in mind, we proposed the Robotic Total Stations Ground Truthing dataset (RTS-GT) dataset to support localization research with the generation of six-Degrees Of Freedom (DOF) ground truth trajectories. This novel dataset includes six-DOF ground truth trajectories generated using a system of three Robotic Total Stations (RTSs) tracking moving robotic platforms. Furthermore, we compare the performance of the RTS-based system to a Global Navigation Satellite System (GNSS)-based setup. The dataset comprises around sixty experiments conducted in various conditions over a period of 17 months, and encompasses over 49 kilometers of trajectories, making it the most extensive dataset of RTS-based measurements to date. Additionally, we provide the precision of all poses for each experiment, a feature not found in the current state-of-the-art datasets. Our results demonstrate that RTSs provide measurements that are 22 times more stable than GNSS in various environmental settings, making them a valuable resource for SLAM benchmark development.