PT Unknown
AU Andrew Nolan
   Daniel Serrano
   Aura Hernandez-Sabate
   Daniel Ponsa
   Antonio Lopez
TI Obstacle mapping module for quadrotors on outdoor Search and Rescue operations
BT International Micro Air Vehicle Conference and Flight Competition
PY 2013
DE UAV
AB Obstacle avoidance remains a challenging task for Micro Aerial Vehicles (MAV), due to their limited payload capacity to carry advanced sensors. Unlike larger vehicles, MAV can only carry light weight sensors, for instance a camera, which is our main assumption in this work. We explore passive monocular depth estimation and propose a novel method Position Aided Depth Estimation(PADE). We analyse PADE performance and compare it against the extensively used Time To Collision (TTC). We evaluate the accuracy, robustness to noise and speed of three Optical Flow (OF) techniques, combined with both depth estimation methods. Our results show PADE is more accurate than TTC at depths between 0-12 meters and is less sensitive to noise. Our findings highlight the potential application of PADE for MAV to perform safe autonomous navigation inunknown and unstructured environments.
ER