Automotive Visual-inertial Navigation

汽车视觉惯性导航

基本信息

批准号：
555601-2020
负责人：
Forbes, James
金额：
$ 3.7万
依托单位：
McGill University
依托单位国家：
加拿大
项目类别：
Alliance Grants
财政年份：
2020
资助国家：
加拿大
起止时间：
2020-01-01 至 2021-12-31
项目状态：
已结题

来源：
https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=704947
关键词：
Automotive Visual inertial Navigation

项目摘要

An autonomous vehicle (AV) must use a suite of sensors, such as cameras and inertial sensors, to not only localize itself in the world, but also the positions of other vehicles, pedestrians, cyclists, and obstacles. The goal of the proposed research is to fuse measurements from various sensors, such as an inertial measurements unit (consisting of a rate gyroscope and an accelerometer), wheel odometer, as well as a monocular camera to design a simultaneous localization and mapping (SLAM) solution for AVs using automotive-grade hardware. The navigation solution must be able to estimate the position of static landmarks, such as road barriers and sign posts, and dynamic landmarks, such as moving vehicles, relative to the host vehicle. The navigation solution designed will be used as an input to a path-planning algorithm to plan paths for the AV around other vehicles and obstacles. As such, the development of a robust and reliable navigation solution is critical to the safety of the AV. The deployment of AVs is quickly becoming a reality in Canada and will have an enormous impact on the development of our economy, environment, and society. It is estimated that the cumulative potential benefits of AVs in Canada totals $65 billion dollars per year (expressed in 2013 Canadian dollars), including collision avoidance, fuel cost savings, and congestion avoidance. The proposed research will contribute to a rapidly expanding network of AV research in Canada. By contributing to research regarding the navigation solution for AVs, it is expected that the outcome of this research will place Canada closer to deploying AVs in Canadian cities.

自动驾驶汽车 (AV) 必须使用一套传感器，例如摄像头和惯性传感器，不仅可以定位自身在世界中的位置，还可以定位其他车辆、行人、骑自行车的人和障碍物的位置。拟议研究的目标是融合来自各种传感器的测量结果，例如惯性测量单元（由速率陀螺仪和加速度计组成）、车轮里程计以及单目相机，以设计同步定位和建图（SLAM）使用汽车级硬件的自动驾驶解决方案。导航解决方案必须能够估计静态地标（例如道路障碍和路标）和动态地标（例如移动车辆）相对于主车辆的位置。设计的导航解决方案将用作路径规划算法的输入，以规划自动驾驶汽车绕过其他车辆和障碍物的路径。因此，开发强大且可靠的导航解决方案对于自动驾驶汽车的安全至关重要。自动驾驶汽车的部署正在加拿大迅速成为现实，将对我们的经济、环境和社会的发展产生巨大影响。据估计，加拿大自动驾驶汽车每年累计潜在效益总计 650 亿美元（以 2013 年加元计算），包括避免碰撞、节省燃料成本和避免拥堵。拟议的研究将有助于加拿大自动驾驶汽车研究网络的快速扩展。通过对自动驾驶汽车导航解决方案的研究做出贡献，预计这项研究的结果将使加拿大更接近在加拿大城市部署自动驾驶汽车。