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

移动机器人技术在过去十年中发展迅速，使自动化采矿、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能够估计移动平台的运动。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。在过去的几年里，多伦多大学一直在开发一种基于激光雷达的运动估计管道，该管道基于跟踪强度图像中的稀疏点集。虽然这项技术效果很好，但它仅限于使用提供这些强度图像的传感器的数据，这些图像并不特别常见，而且通常具有较小的视场。该提案的主要目标是修改这一核心技术，以允许从常见的旋转激光雷达传感器配置中稳健地估计（实时）运动，这些配置在当今许多机器人（例如谷歌自动驾驶汽车）上都能找到。这种传感器设置产生点云数据，而不是强度图像。该项目将与位于加拿大多伦多的世界知名汽车定位公司Applanix公司合作开展。Applanix公司已经是全球领先的船舶、飞机和自动驾驶汽车定位系统供应商。将激光雷达整合到Applanix的产品中，将为公司开辟新的应用领域，特别是在自动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.