Abstract

In this talk I will start with state estimation as my PhD work. Very often, state estimation plays a crucial role in a robotic system serving as a building block for autonomy. Challenges are to carry out state estimation in 6-DOF, in real-time at high frequencies, with high precision, robust to aggressive motion and environmental changes. The proposed state estimation method leverages range, vision, and inertial sensing. Then, I will discuss more recent work regarding autonomous navigation of lightweight UAVs in cluttered environments. For collision avoidance and exploration, the work involves a fast planner which is based on an efficient representation of the environment. The talk will finish with the latest results from the DARPA Subterranean Challenge project.

Biography

Ji Zhang is postdoctoral fellow at the Robotics Institute of CMU. He received his PhD degree in Feb. 2017. His PhD research focused on ego-motion estimation and mapping. His method is ranked #1 on the odometry leaderboard of the internationally well-known KITTI Vision Benchmark, and won the Microsoft Indoor Localization Competition in 2016 and 2017. His recent work expanded to collision avoidance and exploration of aerial vehicles. Ji Zhang was founder and Chief Scientist of Kaarta, a CMU spin-off commercializing 3D lidar mapping and 3D modeling technologies as the outcome of his research work.