AlphaPilot: autonomous drone racing.

This paper presents a novel system for autonomous, vision-based drone racing combining learned data abstraction, nonlinear filtering, and time-optimal trajectory planning. The system has successfully been deployed at the first autonomous drone racing world championship: the . Contrary to traditional drone racing systems, which only detect the next gate, our approach makes use of any visible gate and takes advantage of multiple, simultaneous gate detections to compensate for drift in the state estimate and build a global map of the gates.

INS/EKF-based stride length, height and direction intent detection for walking assistance robots.

We propose an algorithm used to obtain the information on stride length, height difference, and direction based on user's intent during walking. For exoskeleton robots used to assist paraplegic patients' walking, this information is used to generate gait patterns by themselves in on-line. To obtain this information, we attach an inertial measurement unit(IMU) on crutches and apply an extended kalman filter-based error correction method to reduce the phenomena of drift due to bias of the IMU.