The content discusses the development of a novel robot navigation dataset in a botanic garden, focusing on sensor integration, data collection, spatial calibration, time synchronization, ground truth generation, semantic annotation, and example dataset usage. The dataset aims to advance research in robot navigation by providing high-quality reference data for various navigation frameworks.
The rapid advancements in mobile robotics have led to the emergence of various applications like robotaxi and unmanned logistics. Existing algorithms have shown maturity in structured scenarios but face challenges in unstructured environments. The BotanicGarden dataset addresses these challenges by providing comprehensive sensor data collected in diverse natural terrains.
Key points include the acquisition platform details, sensor setup specifications, time synchronization methods, spatial calibration processes, data collection procedures, ground truth map generation techniques, ground truth pose calculation methods using LiDAR-based localization algorithms. Semantic annotation details are also provided along with sample sequence evaluations against state-of-the-art algorithms.
The dataset's versatility is demonstrated through assessments of visual, LiDAR-based, and multi-sensor fusion navigation frameworks against ground truth metrics. The results highlight the dataset's potential as a benchmark for challenging robotic navigation tasks and emphasize the importance of multi-sensor fusion approaches for improved accuracy and robustness.
Future work includes expanding the dataset's coverage and trajectory length while incorporating different weather conditions to enhance its complexity. Additionally, plans involve equipping the platform with independent GNSS receivers for collecting data across all seasons under varying vegetation states to support research on GNSS-integrated navigation systems.
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by Yuanzhi Liu,... at arxiv.org 03-05-2024
https://arxiv.org/pdf/2306.14137.pdfDeeper Inquiries