Spectral analysis for long-term robotic mapping

This paper presents a new approach to mobile robot mapping in long-term scenarios. So far, the environment models used in mobile robotics have been tailored to capture static scenes and dealt with the environment changes by means of ‘memory decay’. While these models keep up with slowly changing environments, their utilization in dynamic, real world environments is difficult. The representation proposed in this paper models the environment’s spatio-temporal dynamics by its frequency spectrum. The spectral representation of the time domain allows to identify, analyse and remember regularly occurring environment processes in a computationally efficient way. Knowledge of the periodicity of the different environment processes constitutes the model predictive capabilities, which are especially useful for long-term mobile robotics scenarios. In the experiments presented, the proposed approach is applied to data collected by a mobile robot patrolling an indoor environment over a period of one week. Three scenarios are investigated, including intruder detection and 4D mapping. The results indicate that the proposed method allows to represent arbitrary timescales with constant (and low) memory requirements, achieving compression rates up to 106 . Moreover, the representation allows for prediction of future environment’s state with ∼ 90% precision

FreMEn: Frequency map enhancement for long-term mobile robot autonomy in changing environments

We present a new approach to long-term mobile robot mapping in dynamic indoor environments. Unlike traditional world models that are tailored to represent static scenes, our approach explicitly models environmental dynamics. We assume that some of the hidden processes that influence the dynamic environment states are periodic and model the uncertainty of the estimated state variables by their frequency spectra. The spectral model can represent arbitrary timescales of environment dynamics with low memory requirements. Transformation of the spectral model to the time domain allows for the prediction of the future environment states, which improves the robot's long-term performance in dynamic environments. Experiments performed over time periods of months to years demonstrate that the approach can efficiently represent large numbers of observations and reliably predict future environment states. The experiments indicate that the model's predictive capabilities improve mobile robot localisation and navigation in changing environments

Frequency map enhancement: introducing dynamics into static environment models

We present applications of the Frequency Map Enhancement (FreMEn), which improves the performance of mobile robots in long-term scenarios by introducing the notion of dynamics into their (originally static) environment models. Rather than using a fixed probability value, the method models the uncertainty of the elementary environment states by their frequency spectra. This allows to integrate sparse and irregular observations obtained during long-term deployments of mobile robots into memory-efficient spatio-temporal models that reflect mid- and long-term pseudo-periodic environment variations. The frequency-enhanced spatio-temporal 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 weeks to years, we demonstrate that the proposed approach improves mobile robot localization, path and task planning, activity recognition and allows for life-long spatio-temporal exploration

A 3D simulation environment with real dynamics: a tool for benchmarking mobile robot performance in long-term deployments

This paper describes a method to compare and evaluate mobile robot algorithms for long-term deployment in changing environments. Typically, the long-term performance of state estimation algorithms for mobile robots is evaluated using pre-recorded sensory datasets. However such datasets are not suitable for evaluating decision-making and control algorithms where the behaviour of the robot will be different in every trial. Simulation allows to overcome this issue and while it ensures repeatability of experiments, the development of 3D simulations for an extended period of time is a costly exercise. In our approach long-term datasets comprising high-level tracks of dynamic entities such as people and furniture are recorded by ambient sensors placed in a real environment. The high-level tracks are then used to parameterise a 3D simulation containing its own geometric models of the dynamic entities and the background scene. This simulation, which is based on actual human activities, can then be used to benchmark and validate algorithms for long-term operation of mobile robots

Lifelong exploration of dynamic environments

We propose a novel spatio-temporal mobile-robot exploration method for dynamic, human-populated environments. In contrast to other exploration methods that model the environment as being static, our spatio-temporal exploration method creates and maintains a world model that not only represents the environment's structure, but also its dynamics over time. Consideration of the world dynamics adds an extra, temporal dimension to the explored space and makes the exploration task a never-ending data-gathering process to keep the robot's environment model up-to-date. Thus, the crucial question is not only where, but also when to observe the explored environment. We address the problem by application of information-theoretic exploration to world representations that model the environment states' uncertainties as probabilistic functions of time. The predictive ability of the spatio-temporal model allows the exploration method to decide not only where, but also when to make environment observations. To verify the proposed approach, an evaluation of several exploration strategies and spatio-temporal models was carried out using real-world data gathered over several months. The evaluation indicates that through understanding of the environment dynamics, the proposed spatio-temporal exploration method could predict which locations were going to change at a specific time and use this knowledge to guide the robot. Such an ability is crucial for long-term deployment of mobile robots in human-populated spaces that change over time

Lifelong information-driven exploration to complete and refine 4-D spatio-temporal maps

This paper presents an exploration method that allows mobile robots to build and maintain spatio-temporal models of changing environments. The assumption of a perpetuallychanging world adds a temporal dimension to the exploration problem, making spatio-temporal exploration a never-ending, life-long learning process. We address the problem by application of information-theoretic exploration methods to spatio-temporal models that represent the uncertainty of environment states as probabilistic functions of time. This allows to predict the potential information gain to be obtained by observing a particular area at a given time, and consequently, to decide which locations to visit and the best times to go there. To validate the approach, a mobile robot was deployed continuously over 5 consecutive business days in a busy office environment. The results indicate that the robot’s ability to spot environmental changes i

FreMEn: frequency map enhancement for long-term mobile robot autonomy in changing environments

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 memory-efficient spatio-temporal models that reflect the mid- to long-term environment dynamics. These frequency-enhanced spatio-temporal 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

Spatio-temporal representation for cognitive control in long-term scenarios

The FP-7 Integrated Project STRANDS [1] is aimed at producing intelligent mobile robots that are able to operate robustly for months in dynamic human environments. To achieve long-term autonomy, the robots would need to understand the environment and how it changes over time. For that, we will have to develop novel approaches to extract 3D shapes, objects, people, and models of activity from sensor data gathered during months of autonomous operation. So far, the environment models used in mobile robotics have been tailored to capture static scenes and environment variations are largely treated as noise. Therefore, utilization of the static models in ever-changing, real world environments is difficult. We propose to represent the environment’s spatio-temporal dynamics by its frequency spectrum

Now or later? Predicting and maximising success of navigation actions from long-term experience

In planning for deliberation or navigation in real-world robotic systems, one of the big challenges is to cope with change. It lies in the nature of planning that it has to make assumptions about the future state of the world, and the robot’s chances of successively accomplishing actions in this future. Hence, a robot’s plan can only be as good as its predictions about the world. In this paper, we present a novel approach to specifically represent changes that stem from periodic events in the environment (e.g. a door being opened or closed), which impact on the success probability of planned actions. We show that our approach to model the probability of action success as a set of superimposed periodic processes allows the robot to predict action outcomes in a long-term data obtained in two real-life offices better than a static model. We furthermore discuss and showcase how this knowledge gathered can be successfully employed in a probabilistic planning framework to devise better navigation plans. The key contributions of this paper are (i) the formation of the spectral model of action outcomes from non-uniform sampling, the (ii) analysis of its predictive power using two long-term datasets, and (iii) the application of the predicted outcomes in an MDP-based planning framework

Warped Hypertime Representations for Long-Term Autonomy of Mobile Robots

This letter presents a novel method for introducing time into discrete and continuous spatial representations used in mobile robotics, by modeling long-term, pseudo-periodic variations caused by human activities or natural processes. Unlike previous approaches, the proposed method does not treat time and space separately, and its continuous nature respects both the temporal and spatial continuity of the modeled phenomena. The key idea is to extend the spatial model with a set of wrapped time dimensions that represent the periodicities of the observed events. By performing clustering over this extended representation, we obtain a model that allows the prediction of probabilistic distributions of future states and events in both discrete and continuous spatial representations. We apply the proposed algorithm to several long-term datasets acquired by mobile robots and show that the method enables a robot to predict future states of representations with different dimensions. The experiments further show that the method achieves more accurate predictions than the previous state of the art