This paper presents a novel approach to vehicle positioning that operates without reliance on the global navigation satellite system (GNSS). Traditional GNSS approaches are vulnerable to interference in certain environments, rendering them unreliable in situations such as urban canyons, under flyovers, or in low reception areas. This study proposes a vehicle positioning method based on learning the road signature from accelerometer and gyroscope measurements obtained by an inertial measurement unit (IMU) sensor. In our approach, the route is divided into segments, each with a distinct signature that the IMU can detect through the vibrations of a vehicle in response to subtle changes in the road surface. The study presents two different data-driven methods for learning the road segment from IMU measurements. One method is based on convolutional neural networks and the other on ensemble random forest applied to handcrafted features. Additionally, the authors present an algorithm to deduce the position of a vehicle in real-time using the learned road segment. The approach was applied in two positioning tasks: (i) a car along a 6[km] route in a dense urban area; (ii) an e-scooter on a 1[km] route that combined road and pavement surfaces. The mean error between the proposed method's position and the ground truth was approximately 50[m] for the car and 30[m] for the e-scooter. Compared to a solution based on time integration of the IMU measurements, the proposed approach has a mean error of more than 5 times better for e-scooters and 20 times better for cars.

翻译：本文提出了一种不依赖全球导航卫星系统（GNSS）的车辆定位新方法。传统GNSS方法在特定环境中易受干扰，在城区峡谷、立交桥下或信号接收较弱的区域可靠性较差。本研究提出了一种基于学习道路特征的车辆定位方法，通过惯性测量单元（IMU）传感器获取加速度计和陀螺仪测量数据。在本方法中，路线被划分为多个路段，每个路段具有独特的特征——IMU可通过检测车辆因路面细微变化而产生的振动来识别这些特征。研究提出了两种基于IMU测量数据学习路段的数据驱动方法：一种基于卷积神经网络，另一种基于集成随机森林对人工设计特征进行学习。此外，作者还提出了一种利用已学习路段实时推导车辆位置的算法。该方法在两个定位任务中得到了验证：（i）密集城区中沿6公里路线行驶的汽车；（ii）在1公里道路与路面混合路线上行驶的电动滑板车。本方法定位结果与真实位置的平均误差：汽车约为50米，电动滑板车约为30米。与基于IMU测量数据时间积分的解决方案相比，本方法在电动滑板车上的平均误差提升超过5倍，在汽车上提升超过20倍。