FreMEn: Frequency Map Enhancement for Long-Term Mobile Robot Autonomy in Changing Environments

Abstract

Abstract-We present a method for introducing representation of dynamics into environment models that were originally tailored to represent static scenes. Rather than using a fixed probability value, the method models the uncertainty of the elementary environment states by probabilistic functions of time. These are composed of combinations of harmonic functions, which are obtained by means of frequency analysis. The use of frequency analysis allows to integrate long-term observations into memoryefficient spatio-temporal models that reflect the mid-to longterm environment dynamics. These frequency-enhanced spatiotemporal models allow to predict the future environment states, which improves the efficiency of mobile robot operation in changing environments. In a series of experiments performed over periods of days to years, we demonstrate that the proposed approach improves localization, path planning and exploration.

Warped Hypertime Representations for Long-Term Autonomy of Mobile Robots

“…Unlike the aforementioned works, which focused on the spatial structure and appearance aspects of the changes observed, other authors [2], [15], [16], [3], [5] focused on modelling the temporal aspects. For example, [2] and [15] represent the environment dynamics by multiple temporal models with different timescales, where the best map for localisation is chosen by its consistency with current readings.…”

“…The aforementioned approaches demonstrated that considering temporal aspects (and especially their persistence and periodicity) in robotic models improves not only mobile robot localisation [5], [3], [2], but also planning [17], [18] and exploration [19]. However, these temporal representations were tailored to model the probability of a single state over time, and thus were applied only to individual components of the discretised models, e.g.…”

“…To compare the efficiency of the predictions, we calculated the mean squared error ε of the various temporal models' predictions p(t) to the ground truth s(t) as ε = ∑ T (p(t) − s(t)) 2 /|s(t)|. The results indicate that both hypertime-based models outperformed the other ones, including FreMEn [5]. Both methods indicated that the most prominent periodicies corresponded to one day, four hours and one week.…”

A Poisson-spectral model for modelling temporal patterns in human data observed by a robot

“…It has been used in many applications, such as in occupancy grids to compress long-term observations [10], in topological maps to improve robotic search [11], and in path planning [12]. The technique can be applied to all models that represent the world as a set of independent components with binary states [13]. We extend the technique by employing both Poisson processes as the counting model to replace the binary states of FreMEn and a new way of selecting the most prominent frequency components of the Fourier spectrum.…”

Untitled

“…Therefore there is no surprise that up to the moment of writing of this paper there were many papers just in this year dealing with the interaction between a mobile robot and its interface with humans and their environment [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. One of the main means of interaction is vision.…”