ShanghaiTech Mapping Robot is All You Need: Robot System for Collecting Universal Ground Vehicle Datasets

This paper presents the ShanghaiTech Mapping Robot, a state-of-the-art unmanned ground vehicle (UGV) designed for collecting comprehensive multi-sensor datasets to support research in robotics, computer vision, and autonomous driving. The robot is equipped with a wide array of sensors including RGB cameras, RGB-D cameras, event-based cameras, IR cameras, LiDARs, mmWave radars, IMUs, ultrasonic range finders, and a GNSS RTK receiver. The sensor suite is integrated onto a specially designed mechanical structure with a centralized power system and a synchronization mechanism to ensure spatial and temporal alignment of the sensor data. A 16-node on-board computing cluster handles sensor control, data collection, and storage. We describe the hardware and software architecture of the robot in detail and discuss the calibration procedures for the various sensors. The capabilities of the platform are demonstrated through an extensive dataset collected in diverse real-world environments. To facilitate research, we make the dataset publicly available along with the associated robot sensor calibration data. Performance evaluations on a set of standard perception and localization tasks showcase the potential of the dataset to support developments in Robot Autonomy.

osmAG: Hierarchical Semantic Topometric Area Graph Maps in the OSM Format for Mobile Robotics

Maps are essential to mobile robotics tasks like localization and planning. We propose the open street map (osm) XML based Area Graph file format to store hierarchical, topometric semantic multi-floor maps of indoor and outdoor environments, since currently no such format is popular within the robotics community. Building on-top of osm we leverage the available open source editing tools and libraries of osm, while adding the needed mobile robotics aspect with building-level obstacle representation yet very compact, topometric data that facilitates planning algorithms. Through the use of common osm keys as well as custom ones we leverage the power of semantic annotation to enable various applications. For example, we support planning based on robot capabilities, to take the locomotion mode and attributes in conjunction with the environment information into account. The provided C++ library is integrated into ROS. We evaluate the performance of osmAG using real data in a global path planning application on a very big osmAG map, demonstrating its convenience and effectiveness for mobile robots.

Cluster on Wheels

This paper presents a very compact 16-node cluster that is the core of a future robot for collecting and storing massive amounts of sensor data for research on Simultaneous Localization and Mapping (SLAM). To the best of our knowledge, this is the first time that such a cluster is used in robotics. We first present the requirements and different options for computing of such a robot and then show the hardware and software of our solution in detail. The cluster consists of 16 nodes of AMD Ryzen 7 5700U CPUs with a total of 128 cores. As a system that is to be used on a Clearpath Husky robot, it is very small in size, can be operated from battery power and has all required power and networking components integrated. Stress tests on the completed cluster show that it performs well.