Optical flow localisation and appearance mapping (OFLAAM) for long-term navigation

Abstract

This paper presents a novel method to use optical flow navigation for long term navigation. Unlike standard SLAM approaches for augmented reality, OFLAAM is designed for Micro Air Vehicles (MAV). It uses a optical flow camera pointing downwards, a IMU and a monocular camera pointing frontwards. That configuration avoids the computational expensive mapping and tracking of the 3D features. It only maps these features in a vocabulary list by a localization module to tackle the optical flow drift and the lose of the navigation estimation. That module, based on the well established algorithm DBoW2, will be also used to close the loop and allow long-term navigation in previously visited areas. The combination of high speed optical flow navigation with a low rate localization algorithm allows fully autonomous navigation for MAV, at the same time it reduces the overall computational load. This framework is implemented in ROS (Robot Operating System) and tested attached to a laptop. A representative scenario is used to validate and analyze the performance of the system.