Active Perception for Outdoor Localisation with an Omnidirectional Camera
Maleen Jayasuriya, Ravindra Ranasinghe, Gamini Dissanayake
Abstract
This paper presents a novel localisation framework based on an omnidirectional camera, targeted at outdoor urban environments. Bearing only information to persistent and easily observable high-level semantic landmarks (such as lamp-posts, street-signs and trees) are perceived using a Convolutional Neural Network (CNN). The framework utilises an information theoretic strategy to decide the best viewpoint to serve as an input to the CNN instead of the full 360° coverage offered by an omnidirectional camera, in order to leverage the advantage of having a higher field of view without compromising on performance. Environmental landmark observations are supplemented with observations to ground surface boundaries corresponding to high-level features such as manhole covers, pavement edges and lane markings extracted from a second CNN. Localisation is carried out in an Extended Kalman Filter (EKF) framework using a sparse 2D map of the environmental landmarks and Vector Distance Transform (VDT) based representation of the ground surface boundaries. This is in contrast to traditional vision only localisation systems that have to carry out Visual Odometry (VO) or Simultaneous Localisation and Mapping (SLAM), since low level features (such as SIFT, SURF, ORB) do not persist over long time frames due to radical appearance changes (illumination, occlusions etc) and dynamic objects. As the proposed framework relies on highlevel persistent semantic features of the environment, it offers an opportunity to carry out localisation on a prebuilt map, which is significantly more resource efficient and robust. Experiments using a Personal Mobility Device (PMD) driven in a representative urban environment are presented to demonstrate and evaluate the effectiveness of the proposed localiser against relevant state of the art techniques.