Segway DRIVE Benchmark: Place Recognition and SLAM Data Collected by A Fleet of Delivery Robots
This provides a more realistic dataset for researchers in visual place recognition and SLAM, though it is incremental as it focuses on data collection rather than new algorithms.
The paper introduces the Segway DRIVE benchmark, a dataset collected by delivery robots to address the gap between academic SLAM research and real-world deployment, featuring over 50 km of indoor data with challenging conditions like moving pedestrians and lighting changes.
Visual place recognition and simultaneous localization and mapping (SLAM) have recently begun to be used in real-world autonomous navigation tasks like food delivery. Existing datasets for SLAM research are often not representative of in situ operations, leaving a gap between academic research and real-world deployment. In response, this paper presents the Segway DRIVE benchmark, a novel and challenging dataset suite collected by a fleet of Segway delivery robots. Each robot is equipped with a global-shutter fisheye camera, a consumer-grade IMU synced to the camera on chip, two low-cost wheel encoders, and a removable high-precision lidar for generating reference solutions. As they routinely carry out tasks in office buildings and shopping malls while collecting data, the dataset spanning a year is characterized by planar motions, moving pedestrians in scenes, and changing environment and lighting. Such factors typically pose severe challenges and may lead to failures for SLAM algorithms. Moreover, several metrics are proposed to evaluate metric place recognition algorithms. With these metrics, sample SLAM and metric place recognition methods were evaluated on this benchmark. The first release of our benchmark has hundreds of sequences, covering more than 50 km of indoor floors. More data will be added as the robot fleet continues to operate in real life. The benchmark is available at http://drive.segwayrobotics.com/#/dataset/download.