Lidar-pointcloud-based motion estimation to support 3D modelling and autonomous route driving

移动的机器人技术在过去十年中发展迅速，使自动采矿、农业、安全和安保以及自动驾驶汽车等应用成为近期现实。 实现这一愿景所需的核心工具之一是从移动平台估计运动的能力。 这种运动估计能力是精确三维建模和自动路线驾驶的关键先决条件。 在过去几年中，多伦多大学一直在开发基于激光雷达的运动估计流水线，该流水线基于跟踪强度图像中的稀疏点集。 虽然这种技术工作得很好，但它限于使用来自提供这些强度图像的传感器的数据，这些强度图像不是特别常见，并且通常具有小的视场。该提议的主要目标是修改该核心技术，以允许从常见的自旋激光雷达传感器配置（在当今的许多机器人（例如，Google自动驾驶汽车）。 这种传感器设置产生点云数据，而不是强度图像。 该项目将与Applanix公司合作进行，Applanix公司是位于加拿大多伦多的一家世界知名的车辆定位公司。 Applanix公司已经是船舶、飞机和自动驾驶汽车定位系统的全球顶级供应商。将激光雷达应用到Applanix的产品中，将为该公司开辟新的应用领域，特别是在自主采矿和自动测量方面。

Mobile robotics technology has advanced at a rapid pace in the last decade, making applications such as automated mining, agriculture, safety and security, and self-driving cars a near-term reality. One of the core tools needed to enable this vision is the ability to estimate motion from a moving platform. This motion-estimation capability is a key prerequisite for accurate three-dimensional modelling and autonomous route driving. Over the last several years, the University of Toronto has been developing a lidar-based motion estimation pipeline based on tracking a sparse set of points in intensity images. While this technique works well, it is limited to using data from sensors that provide these intensity images, which are not particularly common and typically have a small field of view. The main goal of this proposal is to modify this core technique to allow for motion to be robustly estimated (in realtime) from the common spinning-lidar sensor configuration, found on many of today's robot (e.g., the Google self-driving car). This sensor setup produces pointcloud data rather than intensity images. The project will be carried out in partnership with Applanix Corporation, a well-established world player in vehicle positioning located in Toronto, Canada. Applanix Corporation is already a top global provider of positioning systems for ships, aircraft, and self-driving cars. Incorporating lidar into Applanix's products, will open up new applications for the company, particularly in autonomous mining and automated surveying.