Variational map representation for robotics

想象一下，在一个陌生的城市散步，没有地图。当你走路时，你记住了路径：街道名称，建筑物，你所做的转弯以及它们之间的距离，为你的路线创建了一个心理地图。与此同时，您可以使用这种动态表示来估计您的位置，例如，从远处识别教堂尖塔或记住您走了多远。您可以使用这张“地图”在第二天进一步探索这座城市。这种方法在移动的机器人技术中被称为同时定位和映射（SLAM），并且用于创建未知环境的表示。这些地图同时（和稍后）用于估计机器人的位置，而不依赖于外部系统，如全球定位系统。SLAM的基本数学基础现在是已知的：从传感器数据中提取和识别特征，处理不确定的测量，融合来自多个传感器的噪声数据，以及在识别先前访问过的站点时减少累积错误。许多成功的SLAM演示已经在平面世界的假设下进行（3自由度估计）。 但是，与SLAM相关的许多开放问题仍然存在，包括在大规模动态3D环境中估计无约束的传感器运动和地图。拟议的研究计划调查复杂的环境表示策略的发展-从而更有效的算法一般-SLAM问题。特别是，我计划科学地评估现有的地图表示的效率和有效性，并开发一种新的方法，将变分技术模型的环境（即基于能量的隐式表面）内的概率SLAM估计框架。

Imagine going for a walk in an unknown city without a map. As you walk, you memorize the path: street names, buildings, turns that you made and distances between them, creating a mental map of your route. At the same time you use this dynamic representation to estimate your location by recognizing, for example, church steeples from a distance or by remembering how far you have walked. You can use this "map" to explore the city even further on the next day. This approach is known in mobile robotics as Simultaneous Localization and Mapping (SLAM) and is used to create representations of unknown environments. These maps are concurrently (and later) used to estimate the robot's location without relying on external systems such as the Global Positioning System. Basic mathematical foundations of SLAM are now known: extraction and recognition of features from sensor data, dealing with uncertain measurements, fusion of noisy data from multiple sensors, and the reduction of accumulated errors upon recognition of previously visited sites. Numerous successful SLAM demonstrations have been carried out under assumptions of planar worlds (3 degree of freedom estimation). But many open issues related to SLAM remain, including the estimation of unconstrained sensor motion and maps within large-scale, dynamic, 3D environments. The proposed research program investigates the development of sophisticated environment representation strategies--and thus more effective algorithms in general--for the SLAM problem. In particular, I plan to scientifically evaluate the efficiency and effectiveness of existing map representations and to develop a novel method to incorporate variational techniques to model the environment (i.e. energy-based implicit surfaces) within a probabilistic SLAM estimation framework.