Robust Asynchronous Temporal Event Mapping

Abstract

Localisation and mapping relies on the representation and recognition of features or patterns detected in sensor data. An important aspect is the temporal relationship of observations in sensor data streams. This article proposes a new approach for simultaneous localisation and mapping based on temporal relations in the flow of characteristic events in the sensor data channels. A dynamical system is employed to acquire these correlations between simultaneous and sequential events from different sources, to map causal sequences, while considering time spans, and to recognise previously observed patterns (localisation). While this system is applicable to sensor modalities with different characteristics and timing behaviours, it is especially suitable for distributed computing. Mapping and localisation take place simultaneously in an life-long unsupervised distributed on-line learning process. The dynamical system has been implemented as a distributed realtime system with symmetric processes. A real-time clustering network reduces the dimension of raw sensor data; cluster transitions are used as input for the dynamical mapping system. Results from physical experiments with one sensor modality are presented.