This thesis addresses the problem of localization and mapping in mobile robotics. The ability of a robot to build a map of an unknown environment from sensory information is required to perform self-localization and autonomous navigation, as a necessary condition to carry out more complex tasks. This problem has been widely investigated in the last decades, but the solutions presented have still important limitations, mainly to cope with large scale and dynamic environments, and to work in a wider range of conditions and scenarios. In this context, this thesis takes a step forward towards highly efficient localization and mapping. A first contribution of this work is a new mapping strategy that presents two key features: the lightweight representation of world metric information, and the organization of this metric map into a topological structure that allows efficient localization and map optimization. Regarding the first issue, a map is proposed based on planar patches which are extracted from range or RGB-D images. This plane-based map ( PbMap ) is particularly well suited for indoor scenarios, and has the advantage of being a very compact and still a descriptive representation which is useful to perform real-time place recognition and loop closure. These operations are based on matching planar features taking into account their geometric relationships. On the other hand, the abstraction of metric information is necessary to deal with large scale SLAM and with navigation in complex environments. For that, we propose to structure the map in a metric-topological structure which is dynamically organized upon the sensor observations. ?Also, a simultaneous localization and mapping (SLAM) system employing an omnidirectional RGB-D device which combines several structured-light sensors (Asus Xtion Pro Live) is presented. This device allows the quick construction of rich models of the environment at a relative low cost in comparison with previous alternatives. Our SLAM approach is based on a hierarchical structure of keyframes with a low level layer of metric information and several topological layers intended for large scale SLAM and navigation. This SLAM solution, which makes use of the metric-topological representation mentioned above, works at video frame rate obtaining highly consistent maps. Future research is expected on metric-topological-semantic mapping from the new sensor and the SLAM system presented here. Finally, an extrinsic calibration technique is proposed to obtain the relative poses of a combination of 3D range sensors, like those employed in the omnidirectional RGB-D device mentioned above. The calibration is computed from the observation of planar surfaces of a structured environment in a fast, easy and robust way, presenting qualitative and quantitative advantages with respect to previous approaches. This technique is extended to calibrate any combination of range sensors, including 2D and 3D range sensors, in any configuration. The calibration of such sets of sensors is interesting not only for mobile robots, but also for autonomous cars.
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机译：本文解决了移动机器人中的定位和映射问题。作为执行更复杂任务的必要条件，要求机器人具有从感官信息构建未知环境的地图的能力，以执行自我定位和自主导航。在过去的几十年中，已经对该问题进行了广泛的研究，但是提出的解决方案仍然存在重要的局限性，主要是要应对大规模和动态环境以及在更广泛的条件和场景下工作。在这种情况下，本论文向高效的定位和制图迈出了一步。这项工作的第一个贡献是一种新的映射策略，它具有两个关键特征：世界度量信息的轻量表示，以及将此度量映射组织为允许有效定位和地图优化的拓扑结构。关于第一个问题，提出了一种基于从距离图像或RGB-D图像中提取的平面补丁的地图。这种基于平面的地图（PbMap）特别适合于室内场景，并具有非常紧凑且仍具有描述性的表示形式的优点，这对于执行实时位置识别和回路闭合非常有用。这些操作基于匹配平面特征，并考虑了它们的几何关系。另一方面，度量信息的抽象对于处理大规模SLAM和复杂环境中的导航是必要的。为此，我们建议将地图构建为度量-拓扑结构，该结构根据传感器的观察动态组织。还提出了一种同时定位和映射（SLAM）系统，该系统采用了将多个结构化光传感器（Asus Xtion Pro Live）结合在一起的全向RGB-D设备。与先前的替代方案相比，该设备允许以相对较低的成本快速构建丰富的环境模型。我们的SLAM方法基于关键帧的层次结构，该关键帧具有较低级别的度量信息层和几个用于大规模SLAM和导航的拓扑层。该SLAM解决方案利用了上面提到的度量-拓扑表示，以视频帧速率工作，从而获得了高度一致的映射。期望通过此处介绍的新传感器和SLAM系统对度量-拓扑-语义映射进行进一步的研究。最后，提出了一种外部校准技术来获得3D距离传感器组合的相对姿态，就像上面提到的全向RGB-D设备所采用的那样。校准是通过快速，轻松和稳健地观察结构化环境的平面表面而得出的，相对于以前的方法，它在质量和数量上都具有优势。扩展了该技术以校准任何配置中的距离传感器的任何组合，包括2D和3D距离传感器。不仅对于移动机器人，而且对于自动驾驶汽车，这种传感器组的校准都很有趣。
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