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

基于激光雷达点云的运动估计支持 3D 建模和自动路线驾驶

• 批准号: 492024-2015
• 负责人: Barfoot, Timothy
• 金额: $ 3.89万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=690417
• 关键词: Lidar; pointcloud; based; motion; estimation

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.

移动机器人技术技术在过去十年中已经快速发展，从而使自动采矿，农业，安全和保障以及自动驾驶汽车等应用程序成为近期现实。 实现这一愿景所需的核心工具之一是能够从移动平台估算运动。 这种运动估计能力是准确的三维建模和自主路线驾驶的关键先决条件。 在过去的几年中，多伦多大学一直在基于跟踪一组稀疏的强度图像集基于激光雷达的运动估计管道。 尽管该技术运行良好，但它仅限于使用提供这些强度图像的传感器的数据，这些数据并不特别常见，通常具有很小的视野。该提案的主要目的是修改此核心技术，以使运动（实时）从普通旋转的LIDAR传感器配置（例如，Google自动驾驶汽车）上进行稳健估算（实时）。 该传感器设置会产生点云数据而不是强度图像。 该项目将与Applanix Corporation合作进行，Applanix Corporation是位于加拿大多伦多的车辆定位良好的世界参与者。 Applanix Corporation已经是用于船舶，飞机和自动驾驶汽车的定位系统的全球顶级提供商。将LiDAR纳入Applanix的产品中，将为公司开放新的应用程序，尤其是在自主采矿和自动测量方面。