Robust and long-term monocular teach-and-repeat navigation using a single-experience map

This paper presents a robust monocular visual teach-and-repeat (VT&R) navigation system for long-term operation in outdoor environments. The approach leverages deep-learned descriptors to deal with the high illumination variance of the real world. In particular, a tailored self-supervised descriptor, DarkPoint, is proposed for autonomous navigation in outdoor environments. We seamlessly integrate the localisation with control, in which proportional–integral control is used to eliminate the visual error with the pitfall of the unknown depth. Consequently, our approach achieves day-to-night navigation using a single-experience map and is able to repeat complex and fast manoeuvres. To verify our approach, we performed a vast array of navigation experiments in various outdoor environments, where both navigation accuracy and robustness of the proposed system are investigated. The experimental results show that our approach is superior to the baseline method with regards to accuracy and robustness

Predictive and adaptive maps for long-term visual navigation in changing environments

In this paper, we compare different map management techniques for long-term visual navigation in changing environments. In this scenario, the navigation system needs to continuously update and refine its feature map in order to adapt to the environment appearance change. To achieve reliable long-term navigation, the map management techniques have to (i) select features useful for the current navigation task, (ii) remove features that are obsolete, (iii) and add new features from the current camera view to the map. We propose several map management strategies and evaluate their performance with regard to the robot localisation accuracy in long-term teach-and-repeat navigation. Our experiments, performed over three months, indicate that strategies which model cyclic changes of the environment appearance and predict which features are going to be visible at a particular time and location, outperform strategies which do not explicitly model the temporal evolution of the changes.</p

Spatio-temporal object persistence modeling and semantics for long-term robot navigation

Mobile robots increasingly operate in real-world environments that are subject to change over time. Robots that maintain up-to-date, accurate representations of their environment can more robustly perform long-term autonomous navigation and planning tasks. Uninterrupted robot autonomy is important for many tasks where human intervention is undesirable or impossible including space operations, hazardous environment surveillance, military reconnaissance, and more. This dissertation explores how a robot can adapt maps over long periods of time and predict changes in their environments to maintain long-term navigational autonomy. Spatio-temporal Object Persistence (STOP) models enable a robot to assign temporal characteristics to recognized objects based on their observed persistence in the world. A robot develops these identifying characteristics by iteratively estimating parameters for a Weibull distribution survival model using recursive Bayesian Survival Analysis with Markov Chain Monte Carlo methods. The parameters of the model provide temporally identifying features for semantic classification of objects. These characteristics then allow a robot to estimate the true lifespan of objects and predict when they will leave the environment. A robot updates its belief map based on the modelled temporal behavior of objects in its environment and then perform more intelligent planning and navigation for future operations. Furthermore, once the robot develops temporal class characteristics, it can transfer and apply these characteristics to objects in dynamically similar environments, thus allowing the robot to adapt more quickly to new operational spaces. In this dissertation, we establish the efficacy of STOP models for temporal semantic object classification and show how a robot uses them to adapt a map over time in a semi-static environment. We provide a series of experiments to demonstrate various aspects of our implementation. Our results confirm that the learned models not only predict the temporal behavior of objects in the world but also transfer to unknown, but temporally similar operation spaces where they improve the prediction process.Mechanical Engineerin