Robust Navigation for Industrial Service Robots

Abstract

As one of the fundamental problems of robotics, the different challenges that constitute navigation have been studied for decades. Robust, reliable and safe navigation is a key factor for the enablement of higher level functionalities for robots that are going to evolve around humans on a daily basis. Throughout the present thesis, we tackle the problem of navigation for robotic industrial mobile-bases. We identify its components and analyze their respective challenges in order to address them. The research work presented here ultimately aims at improving the overall quality of the navigation stack of a commercially available industrial mobile-base. To introduce and survey the overall problem we first break down the navigation framework into clearly identified smaller problems. We examine the problem of simultaneously mapping the environment and localizing the robot in it by exploring the state of the art. Doing so we recall and detail the mathematical grounding of the Simultaneous Localization and Mapping (SLAM) problem. We then review the problem of planning the trajectory of a mobile-base toward a desired goal in the generated environment representation. Finally we investigate and clarify the concepts and mathematical tools of the Lie theory, which we use extensively to provide rigorous mathematical foundation to our developments, focusing on the subset of the theory that is useful to state estimate in robotics. As the first identified space for improvements, the problem of place recognition for closing loops in SLAM is addressed. Loop closure concerns the ability of a robot to recognize a previously visited location and infer geometrical information between its current and past locations. Using only a 2D laser range finder sensor, the task is challenging as the perception of the environment provided by the sensor is sparse and limited. We tackle the problem using a technique borrowed from the field of Natural Language Processing (NLP) which has been successfully applied to image-based place recognition, namely the Bag-of-Words. We further improve the method with two proposals inspired from NLP. Firstly the comparison of places is strengthen by taking into account the natural relative order of features in each individual sensor readings. Secondly, topological correspondences between places in a corpus of visited places are established in order to promote together instances that are ‘close’ to one another. We evaluate both our proposals separately and jointly on several data sets, with and without noise, and show an improvement over the state of the art.