Robotic Total Stations Ground Truthing Dataset for SLAM Evaluation

Centrala begrepp

Robotic Total Stations (RTS) provide more stable and accurate measurements compared to Global Navigation Satellite System (GNSS), making them valuable for SLAM benchmark development.

Sammanfattning

The Robotic Total Stations Ground Truthing dataset (RTS-GT) aims to support localization research by providing six-Degrees Of Freedom (DOF) ground truth trajectories. The dataset includes over 49 kilometers of trajectories collected in various conditions over 17 months. It compares the performance of RTS-based systems to GNSS-based setups, demonstrating that RTSs are 22 times more stable than GNSS in different environmental settings. The dataset offers precision information for all poses in each experiment, a feature lacking in current datasets. The study highlights challenges faced during data collection, such as leveling RTSs on snow, dealing with occlusions between prisms and RTSs, and mitigating vibrations affecting positioning accuracy.

Statistik

The precision on the final pose is estimated by the same method used in our previous work [12].
Results indicate that RTS precision is stable across multiple environments.
GNSS can have a variation of more than 300 mm in forest environments.
The median precision of the RTS system is approximately 4.5 mm.
The GNSS system exhibits a median precision around 118.1 mm.

Citat

"The results demonstrate that RTSs provide measurements that are 22 times more stable than GNSS in various environmental settings."
"RTS systems deliver more precise and reproducible data compared to GNSS solutions."

Viktiga insikter från

RTS-GT

by Maxi... på arxiv.org 03-14-2024

https://arxiv.org/pdf/2309.11935.pdf

Djupare frågor

How can the use of active prisms improve the generation of ground truth trajectories

The use of active prisms can significantly enhance the generation of ground truth trajectories in several ways. Firstly, active prisms equipped with LEDs emitting unique infrared signatures allow multiple Robotic Total Stations (RTSs) to automatically track different prisms within their field of view. This automation eliminates the need for manual tracking and ensures continuous and accurate data collection during dynamic movements. Secondly, by utilizing active prisms in a multi-RTS setup, it becomes possible to obtain six-Degrees Of Freedom (DOF) reference trajectories in real-time. This advancement enables the generation of precise and detailed ground truth data for moving robotic platforms without sacrificing accuracy or stability.

What are the implications of the stability and accuracy differences between RTS and GNSS systems for SLAM algorithms

The differences in stability and accuracy between RTS and GNSS systems have significant implications for Simultaneous Localization and Mapping (SLAM) algorithms. The superior stability and precision offered by RTS systems compared to GNSS setups can lead to more reliable ground truth data for evaluating SLAM algorithm performance. With RTS providing measurements that are 22 times more stable than GNSS across various environmental settings, SLAM algorithms relying on this high-quality ground truth will likely exhibit improved localization accuracy and mapping capabilities. Additionally, the availability of datasets like RTS-GT showcasing these differences allows researchers to develop SLAM algorithms that are better suited for real-world applications where precise localization is crucial.

How might advancements in ground truth datasets like RTS-GT impact future developments in robotics and autonomous systems

Advancements in ground truth datasets such as RTS-GT hold great promise for shaping future developments in robotics and autonomous systems. By providing extensive six-DOF reference trajectories generated using state-of-the-art technology like Robotic Total Stations, datasets like RTS-GT offer a benchmarking standard that pushes the boundaries of precision measurement capabilities in robotics research. These advancements not only facilitate the evaluation of existing SLAM algorithms but also inspire innovation towards developing more robust navigation solutions for autonomous vehicles, drones, and other robotic platforms operating in diverse environments. Ultimately, access to high-quality ground truth datasets fosters progress in enhancing localization accuracy, mapping efficiency, and overall autonomy levels in robotics applications.