The Tiercel: A novel autonomous micro aerial vehicle that can map the environment by flying into obstacles
Yash Mulgaonkar, Wenxin Liu, Dinesh Thakur, Kostas Daniilidis, Camillo J. Taylor, Vijay Kumar
Abstract
Autonomous flight through unknown environments in the presence of obstacles is a challenging problem for micro aerial vehicles (MAVs). A majority of the current state-of-art research assumes obstacles as opaque objects that can be easily sensed by optical sensors such as cameras or LiDARs. However in indoor environments with glass walls and windows, or scenarios with smoke and dust, robots (even birds) have a difficult time navigating through the unknown space.In this paper, we present the design of a new class of micro aerial vehicles that achieves autonomous navigation and are robust to collisions. In particular, we present the Tiercel MAV: a small, agile, light weight and collision-resilient robot powered by a cellphone grade CPU. Our design exploits contact to infer the presence of transparent or reflective obstacles like glass walls, integrating touch with visual perception for SLAM. The Tiercel is able to localize using visual-inertial odometry (VIO) running on board the robot with a single downward facing fisheye camera and an IMU. We show how our collision detector design and experimental set up enable us to characterize the impact of collisions on VIO. We further develop a planning strategy to enable the Tiercel to fly autonomously in an unknown space, sustaining collisions and creating a 2D map of the environment. Finally we demonstrate a swarm of three autonomous Tiercel robots safely navigating and colliding through an obstacle field to reach their objectives.