Localising PMDs through CNN Based Perception of Urban Streets

The main contribution of this paper is a novel Extended Kalman Filter (EKF) based localisation scheme that fuses two complementary approaches to outdoor vision based localisation. This EKF is aided by a front end consisting of two Convolutional Neural Networks (CNNs) that provide the necessary perceptual information from camera images. The first approach involves a CNN based extraction of information corresponding to artefacts such as curbs, lane markings, and manhole covers to localise on a vector distance transform representation of a binary image of these ground surface boundaries. The second approach involves a CNN based detection of common environmental landmarks such as tree trunks and light poles, which are represented as point features on a sparse map. Utilising CNNs to obtain higher level information about the environment enables this framework to avoid the typical pitfalls of common vision based approaches that use low level hand crafted features for localisation. The EKF framework makes it possible to deal with false positives and missed detections that are inevitable in a practical CNN, to produce a location estimate together with its associated uncertainty. Experiments using a Personal Mobility Device (PMD) driven in typical suburban streets are presented to demonstrate the effectiveness of the proposed localiser.