Engineering Robust 3D Representations from Robotic Visual Sensors for Navigation & Scene Analysis

无论我们是走路还是开车，我们都在不断地制作心理导图，以确定路径/道路、地标、物体和其他人类代理人所在的位置以及它们之间的关系。这些心理地图和模型让我们能够导航到特定的位置，开车去上班，修剪草坪或打扫房屋。当我们推理或计划某些活动时，比如修理栅栏或粉刷房屋，我们也会建立心理导图。将为机器人构建这样一幅心理地图的能力转移到机器人身上，是我们研究的动力。人类主要能够用我们的眼睛做到这一点。如果我们可以为一个有摄像头的机器人做同样的事情，会怎么样？然后，机器人将能够理解世界并进行推理，从而执行一项任务。心理导图只是时间的快照。这张地图还必须包括区分移动实体和静态地标和占位符。根据手头任务的不同，详细程度会有所不同。静态组件的完整3D重建与对象和环境的分析、重新设计或可能的3D打印相关。用于构建3D地图的两种视觉技术包括SFM(运动结构)和SLAM(同步定位和地图绘制)。这两种方法非常相似，区别在于SFM通常是离线的，而SLAM是在线的。这两种方法都包括一个前端，该前端检测感兴趣的要素，并使用摄影测量学在视图之间关联这些数据点。这两种方法的后端都是最小化重新投影误差的优化方法。这两个过程可能容易，也可能困难，具体取决于所使用的传感器、环境中的复杂程度和所需的性能。自动驾驶汽车得益于使用激光雷达传感器和GPS，前者提供精确的环境测量，后者提供位置信息。然而，对于许多现实世界的应用程序来说，仅仅依靠可视化的SLAM/SFM是不可靠的。当摄像机出现锐角时，SLAM/SFM解决方案可能非常脆弱，例如在写字楼中，姿势跟踪失败，无法注册视图。在很大程度上依赖于只有一个摄像系统的工业清洁工的特征点数据关联；(2)为道路、桥梁等市政基础设施监测建立历史地图。强大的SLAM/SFM解决方案可以帮助解决这些应用程序和其他应用程序。

Whether we are walking or driving, we are constantly making mental maps to determine where the pathway/road, landmarks, objects and other human agents are situated and their relationships to each other. These mental maps and models are what allow us to navigate to a particular location, drive to work, mow our lawns or clean our homes. We also build mental maps when we reason or plan certain activities like repairing a fence or painting a home. Transferring the ability to build such a mental map for a robot is what drives our research. Humans are able to chiefly do this with our eyes. What if we could do the same for a robot with a camera? The robot would then be able to understand and reason in the world so as to perform a task. A mental map is just a snapshot in time. This map has to also include differentiating moving entities from static landmarks and placeholders. Depending on the task at hand, the level of detail will vary. A complete 3D reconstruction of the static components is relevant for analysis, re-engineering or possibly 3D printing the objects and environments. Two visual techniques that are used to build 3D maps include SFM (Structure from Motion) and SLAM (Simultaneous Localization And Mapping). The two methods are very similar, the differentiating factor being that SFM is typically off-line while SLAM is online. Both methods include a front end which detects features of interest and uses photogrammetry to associate these data points between views. The back-end for both methods is an optimization method that minimizes re-projection errors. Both processes may be easy or difficult depending on the sensors being used, the level of complexity in the environment and the required performance. Autonomous automobiles benefit from using a LIDAR sensor which provides precise environmental measurements and a GPS which provides location information. However, for many real world applications, just relying on visual SLAM/SFM is not robust. SLAM/SFM solutions can be very brittle when the camera makes sharp corners such as in an office building where the tracking of pose fails and views cannot be registered. There is a heavy reliance on the feature point data association industrial cleaners with only a camera system; (2) building historical maps for municipal infrastructure monitoring such as for roads, bridges; and others. A robust SLAM/SFM solution can help solve these applications and others.