Long-term topological localisation for service robots in dynamic environments using spectral maps

This paper presents a new approach for topological localisation of service robots in dynamic indoor environments. In contrast to typical localisation approaches that rely mainly on static parts of the environment, our approach makes explicit use of information about changes by learning and modelling the spatio-temporal dynamics of the environment where the robot is acting. The proposed spatio-temporal world model is able to predict environmental changes in time, allowing the robot to improve its localisation capabilities during long-term operations in populated environments. To investigate the proposed approach, we have enabled a mobile robot to autonomously patrol a populated environment over a period of one week while building the proposed model representation. We demonstrate that the experience learned during one week is applicable for topological localization even after a hiatus of three months by showing that the localization error rate is significantly lower compared to static environment representations

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

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

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

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

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

Lifelong information-driven exploration for mobile robots to complete and Refine spatio-temporal maps in changing environments

Recent improvements in the ability of mobile robots to operate safely in human populated environments have allowed their deployment in households, offices and public buildings, such as museums and hospitals. However, the structure of these environments is typically not known a priori, which requires the robots to build their own models of their operational environments. This process is commonly known as "exploration" in mobile robotics. Moreover, real-world environments tend to change over time, which means that to achieve long-term autonomous operation, robots must also update their environment models as a part of their daily routine. The assumption of a perpetually-changing world adds a temporal dimension to the exploration problem, making exploration a never-ending lifelong learning process. To the best of our knowledge, this process termed "lifelong exploration" has never been studied in detail before and forms the main topic of the work presented in this thesis. Effcient lifelong exploration requires a robot to choose the right locations and times at which to collect observations in order to improve its environment model. To evaluate the ability of a robot to build and maintain its environment models, we need to be able to compare lifelong exploration strategies under repeatable experimental conditions. An evaluation methodology based on pre-recorded sensory datasets would not be suitable for this purpose, as this would not allow the robot to choose the location or time of its observations. Evaluating lifelong exploration requires the deterministic reproduction of environment changes, while preserving the robots ability to decide upon its own actions during the experiment. This thesis therefore contributes a new benchmarking methodology for lifelong exploration, which replicates the events occurring in real environments through physical simulations that reflect the environment changes gathered by ambient sensors over long periods of time. The established experimental benchmarks are based on long-term sensory datasets recorded in a smart home, with dynamics produced by a single person over a period of one year, and an office environment, with dynamics produced by a team of workers. Building upon the aforementioned benchmarking methodology, the thesis investigates possible strategies for lifelong exploration. An experimental comparison of lifelong exploration strategies that combine various decision-making paradigms and spatio-temporal representations is presented. Moreover, a new approach to lifelong explorations is proposed that applies information-theoretic exploration techniques to environment representations that model the uncertainty of world states as probabilistic functions of time. The proposed method explicitly models the world dynamics and can predict the environment changes. The predictive ability is used to reason about the most informative locations to explore for a given time. A 16 week long experiment shows that the combination of dynamic environment representations with information-gain exploration principles allows to create and maintain up-to-date models of continuously changing environments, enabling efficient and self-improving long-term operation of mobile service robots. The final part of the thesis considers the problem of acquiring and maintaining dense 3D models of dynamic environments during long-term operation, building on the work presented in the earlier chapters. The term "4D mapping" is used to indicate 3D mapping by mobile robots over extended periods of time. A new approach to lifelong 4D mapping and exploration is presented, which was deployed on a real robotic platform during long term operation in real-world human-populated environments. The approach integrates sensory data captured by the robot at different times and locations into a global, metric I 4D spatio-temporal model and then uses the model to decide where and when to perform the next round of observations. Finally, the deployment of the 4D exploration method in a real-world office scenario is described and evaluated. The one week long experiments show that the method enables reliable 4D mapping and persistent self-localisation of autonomous mobile robots, continually improving the robots maps to reflect the ever-changing world

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