论文标题
IO-VNBD：用于地面车辆定位的惯性和探测基准数据集
IO-VNBD: Inertial and Odometry Benchmark Dataset for Ground Vehicle Positioning
论文作者
论文摘要
低成本惯性导航传感器（INS）可以用于自动驾驶汽车的可靠跟踪解决方案。但是，由于测量值中的噪声，位置误差呈指数增长。已经研究了几种深度学习技术，以减轻更好的导航解决方案的错误[1-10]。但是，这些研究涉及使用不同数据集的使用未公开可用。因此，缺乏强大的基准数据集妨碍了基于惯性导航的研究，比较和采用用于车辆定位的深度学习技术的进步。因此，为了促进定位算法的基准测试，快速开发和评估，我们介绍了第一个大型和信息丰富和信息丰富的惯性和验光量为IO-VNBD的集中的公共数据集（惯性矛盾的车辆导航型载量（惯性矛盾车辆导航基准数据）。尼日利亚和法国。这些传感器包括GPS接收器，惯性导航传感器，汽车上的其他传感器以及在10Hz的Android智能手机采样中的其他传感器以及惯性导航传感器和GPS接收器。捕获了各种各样的场景和车辆动力学，例如在不同的道路类型（乡村道路，高速公路等）上使用不同的驾驶方式上的交通，圆形，易碎等。该数据集的总驾驶时间约为1,300公里的40公里，用于车辆提取的数据，在智能手机录制的数据中大约在4,400公里的时间内约58小时。我们希望该数据集将证明对发展有关车辆动态与其位移以及其他相关研究之间相关性的研究很有价值
Low-cost inertial navigation sensors (INS) can be exploited for a reliable tracking solution for autonomous vehicles. However, position errors grow exponentially due to noises in the measurements. Several deep learning techniques have been investigated to mitigate the errors for a better navigation solution [1-10]. However, these studies have involved the use of different datasets not made publicly available. The lack of a robust benchmark dataset has thus hindered the advancement in the research, comparison and adoption of deep learning techniques for vehicle positioning based on inertial navigation. In order to facilitate the benchmarking, fast development and evaluation of positioning algorithms, we therefore present the first of its kind large-scale and information-rich inertial and odometry focused public dataset called IO-VNBD (Inertial Odometry Vehicle Navigation Benchmark Dataset).The vehicle tracking dataset was recorded using a research vehicle equipped with ego-motion sensors on public roads in the United Kingdom, Nigeria, and France. The sensors include a GPS receiver, inertial navigation sensors, wheel-speed sensors amongst other sensors found on the car as well as the inertial navigation sensors and GPS receiver in an android smart phone sampling at 10HZ. A diverse number of scenarios and vehicle dynamics are captured such as traffic, round-abouts, hard-braking etc. on different road types (country roads, motorways etc.) with varying driving patterns. The dataset consists of a total driving time of about 40 hours over 1,300km for the vehicle extracted data and about 58 hours over 4,400 km for the smartphone recorded data. We hope that this dataset will prove valuable in furthering research on the correlation between vehicle dynamics and its displacement as well as other related studies
