A Benchmark Dataset for Collaborative SLAM in Service Environments

Harin Park, Inha Lee, Minje Kim, Hyungyu Park, Kyungdon Joo

Abstract: We introduce a new multi-modal collaborative SLAM (C-SLAM) dataset for multiple service robots in various indoor service environments, called C-SLAM dataset in Service Environments (CSE). We use the NVIDIA Isaac Sim to generate data in various indoor service environments with the challenges that may occur in real-world service environments. By using the simulator, we can provide precisely time-synchronized sensor data, such as stereo RGB/depth, IMU, and ground truth (GT) poses. We configure three common indoor service environments (Hospital, Office, and Warehouse), each featuring dynamic objects performing motions suited to the environment. In addition, we drive the robots to mimic the actions of real service robots. Through these factors, we generate a realistic C-SLAM dataset for multiple service robots. We demonstrate our CSE dataset by evaluating diverse state-of-the-art single-robot SLAM and multi-robot SLAM methods. Additionally, we provide a detailed tutorial on generating C-SLAM data using the simulator.