IEEE Trans Pattern Anal Mach Intell. 2016 Apr;38(4):652-65. doi: 10.1109/TPAMI.2015.2453975.
Accurate and efficient self-localization is a critical problem for autonomous systems. This paper describes an affordable solution to vehicle self-localization which uses odometry computed from two video cameras and road maps as the sole inputs. The core of the method is a probabilistic model for which an efficient approximate inference algorithm is derived. The inference algorithm is able to utilize distributed computation in order to meet the real-time requirements of autonomous systems in some instances. Because of the probabilistic nature of the model the method is capable of coping with various sources of uncertainty including noise in the visual odometry and inherent ambiguities in the map (e.g., in a Manhattan world). By exploiting freely available, community developed maps and visual odometry measurements, the proposed method is able to localize a vehicle to 4 m on average after 52 seconds of driving on maps which contain more than 2,150 km of drivable roads.
准确高效的自定位是自主系统的关键问题。本文描述了一种经济实惠的车辆自定位解决方案，该方案仅使用两个摄像机和道路图计算的里程计作为输入。该方法的核心是一个概率模型，从中推导出一种有效的近似推理算法。推理算法能够利用分布式计算，以满足某些情况下自主系统的实时要求。由于模型的概率性质，该方法能够处理各种来源的不确定性，包括视觉里程计中的噪声和地图中的固有歧义（例如，在曼哈顿世界中）。通过利用免费提供的、社区开发的地图和视觉里程计测量，所提出的方法能够在驾驶超过 2150 公里可行驶道路的地图 52 秒后，将车辆定位到平均 4 米的位置。
