We present a novel concept for teach-and-repeat visual navigation. The proposed concept is based on a mathematical model, which indicates that in teach-and-repeat navigation scenarios, mobile robots do not need to perform explicit localisation. Rather than that, a mobile robot which repeats a previously taught path can simply "replay" the learned velocities, while using its camera information only to correct its heading relative to the intended path. To support our claim, we establish a position error model of a robot, which traverses a taught path by only correcting its heading. Then, we outline a mathematical proof which shows that this position error does not diverge over time. Based on the insights from the model, we present a simple monocular teach-and-repeat navigation method. The method is computationally efficient, it does not require camera calibration, and it can learn and autonomously traverse arbitrarily-shaped paths. In a series of experiments, we demonstrate that the method can reliably guide mobile robots in realistic indoor and outdoor conditions, and can cope with imperfect odometry, landmark deficiency, illumination variations and naturally-occurring environment changes. Furthermore, we provide the navigation system and the datasets gathered at www.github.com/gestom/stroll_bearnav.

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This paper presents a novel method for introducing time into discrete and continuous spatial representations used in mobile robotics, by modelling long-term, pseudoperiodic variations caused by human activities. 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 method extends the given spatial model with a set of wrapped dimensions that represent the periodicities of observed changes. By performing clustering over this extended representation, we obtain a model that allows us to predict future states of both discrete and continuous spatial representations. We apply the proposed algorithm to several long-term datasets 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.

We present a human-centric spatio-temporal model for service robots operating in densely populated environments for long time periods. The method integrates observations of pedestrians performed by a mobile robot at different locations and times into a memory efficient model, that represents the spatial layout of natural pedestrian flows and how they change over time. To represent temporal variations of the observed flows, our method does not model the time in a linear fashion, but by several dimensions wrapped into themselves. This representation of time can capture long-term (i.e. days to weeks) periodic patterns of peoples' routines and habits. Knowledge of these patterns allows making long-term predictions of future human presence and walking directions, which can support mobile robot navigation in human-populated environments. Using datasets gathered by a robot for several weeks, we compare the model to state-of-the-art methods for pedestrian flow modelling.

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Agricultural holdings select goals in various areas when setting their strategic objectives. Economic objectives tend to be viewed as strategic because of the requirement to maximise economic profit for the owners. Since there is significant interaction between agricultural holdings and the environment, it is also important to monitor the environmental aspects of farming. The article seeks to draw on unique multicriteria assessment to compare the compatibility of economic and environmental objectives at 1 189 agricultural holdings in the Czech Republic, broken down by farming specialisation and economic size on the basis of figures from the Farm Accountancy Data Network (FADN). A trade-off between environmental sustainability and economic performance occurs primarily among farming specialisation categories, where we found two extremes – intensive field cropping with high economic performance and low environmental sustainability, and, at the other end of the scale, extensive cattle farming with lower economic performance and high environmental sustainability. Within the farming specialisation categories, however, there was no significant correlation, with the exception of milk production, where the use of soil organic matter, a higher proportion of soil improving crops (for fodder) and greening made a positive contribution to the higher economic performance of farms.