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

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

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

Modeling and interpolation of the ambient magnetic field by Gaussian processes

Anomalies in the ambient magnetic field can be used as features in indoor positioning and navigation. By using Maxwell's equations, we derive and present a Bayesian non-parametric probabilistic modeling approach for interpolation and extrapolation of the magnetic field. We model the magnetic field components jointly by imposing a Gaussian process (GP) prior on the latent scalar potential of the magnetic field. By rewriting the GP model in terms of a Hilbert space representation, we circumvent the computational pitfalls associated with GP modeling and provide a computationally efficient and physically justified modeling tool for the ambient magnetic field. The model allows for sequential updating of the estimate and time-dependent changes in the magnetic field. The model is shown to work well in practice in different applications: we demonstrate mapping of the magnetic field both with an inexpensive Raspberry Pi powered robot and on foot using a standard smartphone.Comment: 17 pages, 12 figures, to appear in IEEE Transactions on Robotic