Applying Rule-Based Context Knowledge to Build Abstract Semantic Maps of Indoor Environments

In this paper, we propose a generalizable method that systematically combines data driven MCMC samplingand inference using rule-based context knowledge for data abstraction. In particular, we demonstrate the usefulness of our method in the scenario of building abstract semantic maps for indoor environments. The product of our system is a parametric abstract model of the perceived environment that not only accurately represents the geometry of the environment but also provides valuable abstract information which benefits high-level robotic applications. Based on predefined abstract terms,such as type and relation, we define task-specific context knowledge as descriptive rules in Markov Logic Networks. The corresponding inference results are used to construct a priordistribution that aims to add reasonable constraints to the solution space of semantic maps. In addition, by applying a semantically annotated sensor model, we explicitly use context information to interpret the sensor data. Experiments on real world data show promising results and thus confirm the usefulness of our system.Comment: arXiv admin note: text overlap with arXiv:2002.0840

Robot learning for loop closure detection and SLAM

Includes bibliographical references.2019 Fall.Robotics and autonomy continues to be a key research and development focus around the world. Robots are increasingly prevalent in everyday life. From manufacturing, home cleaning, to self-driving vehicles, robots are an ever-present reality with demonstrated ca- pability to increase quality of life for humans. As more and more robots exist surrounding humans, it becomes increasingly critical that robots can accurately sense and reason about the environment. The functionality of a robot building a map of its environment and lo- cating itself constantly within the map is known as Simultaneous Localization and Mapping (SLAM). SLAM is a difficult problem, and can be especially challenging when environmental appearance changers occur or when a GPS signal is not available. However, it’s within these challenging environments where the use of robots is critical. Consider a partially collapsed underground mine environment. If the environment is potentially dangerous, it doesn’t make sense to risk human life to enter the mine to perform search and rescue. If robots can be enabled to operate in challenging environments such as collapsed mines, human life can be saved. This Master’s thesis addresses the problem of increasing the effectiveness of SLAM in these challenging environments. First, I describe a data structure capable of capturing environmental metadata for semantic description overlay to augment mapping capability. Secondly, I introduce a novel loop closure detection technique that utilizes robot learning to understand complex environments. These efforts combined contribute to increasing the effectiveness of SLAM in GPS-denied environments or environments with varying lighting conditions

A Generalizable Knowledge Framework for Semantic Indoor Mapping Based on Markov Logic Networks and Data Driven MCMC

In this paper, we propose a generalizable knowledge framework for data abstraction, i.e. finding compact abstract model for input data using predefined abstract terms. Based on these abstract terms, intelligent autonomous systems, such as a robot, should be able to make inference according to specific knowledge base, so that they can better handle the complexity and uncertainty of the real world. We propose to realize this framework by combining Markov logic networks (MLNs) and data driven MCMC sampling, because the former are a powerful tool for modelling uncertain knowledge and the latter provides an efficient way to draw samples from unknown complex distributions. Furthermore, we show in detail how to adapt this framework to a certain task, in particular, semantic robot mapping. Based on MLNs, we formulate task-specific context knowledge as descriptive soft rules. Experiments on real world data and simulated data confirm the usefulness of our framework