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In this talk, I will present work on autonomous exploration and introduce our full autonomy system. The work started several years ago from lidar-based state estimation. Building upon the state estimation module, the autonomy system now contains multiple fundamental modules, e.g. collision avoidance, terrain traversability analysis, and waypoint following. At the high level of navigation planning, an exploration planner consisting of a dual-resolution representation guides the robot to cover and map unknown environments. In addition to autonomous exploration, I will showcase another navigation modality, waypoint navigation, using a simulation demo. The series of work has won both the Best Paper Award and Best System Paper Award of RSS 2021, the only time in the history of RSS that one paper collected both major awards, and the Best Student Paper Award of IROS 2022. The extended journal version has recently been published at Science Robotics. Aimed at lowering the bar for everybody to acquire autonomy and make further use of it, our system is open-sourced at cmu-exploration.com.
Ji Zhang's research interests are in robotic navigation, spanning localization, mapping, planning, and exploration. His early work on Lidar Odometry and Mapping (LOAM) and the succeeding work leveraging range, vision, and inertial sensing ignited real-time 3D Lidar SLAM. His work ranked #1 on the odometry leaderboard of KITTI Vision Benchmark between 2014 and 2021. He founded Kaarta, Inc, a CMU spin-off commercializing 3D mapping & modeling technologies, and stayed with the company for 4 years as chief scientist. In late 2019, He rejoined CMU as a faculty member and started leading the development of a series of autonomous navigation algorithms. The complete navigation system brought CMU-OSU Team a "Most Sectors Explored Award" on DARPA Subterranean Challenge. He currently holds another role at the National Robotics Engineering Center at CMU and leads a few serious projects.