Ma Wenzhuo, Yue Zhe, Lian Zengzeng, Li Kezhao, Sun Chenchen, Zhang Mengshuo
School of Surveying and Mapping, Henan Polytechnic University, Jiaozuo 454003, China.
Sensors (Basel). 2024 Dec 16;24(24):8019. doi: 10.3390/s24248019.
Amidst the backdrop of the profound synergy between navigation and visual perception, there is an urgent demand for accurate real-time vehicle positioning in urban environments. However, the existing global navigation satellite system (GNSS) algorithms based on Kalman filters fall short of precision. In response, we introduce an elastic filtering algorithm with visual perception for vehicle GNSS navigation and positioning. Firstly, the visual perception system captures real-time environmental data around the vehicle. It utilizes the interframe differential optical flow method and vehicle state switching characteristics to assess the current driving status. Secondly, we design an elastic filtering model specifically for various vehicle states. This model enhances the precision of Kalman filter-based GNSS navigation. In urban driving, vehicles often experience frequent stationary parking. To address this, we incorporate a zero-speed constraint to further refine vehicle location data when the vehicle is stationary. This constraint matches the data with the appropriate elastic filtering model. Ultimately, we conduct simulation and real-world vehicle navigation experiments to confirm the validity and rationality of our proposed algorithm. Compared with the conventional algorithm and the existing interactive multi-model algorithm, the proposed algorithm significantly improves the navigation and positioning accuracy of vehicle GNSS in urban environments. Compared to the commonly used constant acceleration (CA) and Constant Velocity (CV) models, there has been a significant improvement in positioning accuracy. Furthermore, when benchmarked against the more advanced interactive multi-model (IMM) model, the method proposed in this paper has enhanced the positioning accuracy enhancements in three dimensions: 21.8%, 20.9%, and 31.3%, respectively.
在导航与视觉感知深度协同的背景下，城市环境中对车辆精确实时定位有着迫切需求。然而，现有的基于卡尔曼滤波器的全球导航卫星系统（GNSS）算法精度不足。对此，我们引入一种用于车辆GNSS导航与定位的具有视觉感知的弹性滤波算法。首先，视觉感知系统捕捉车辆周围的实时环境数据。它利用帧间差分光流法和车辆状态切换特性来评估当前驾驶状态。其次，我们针对各种车辆状态设计了一个弹性滤波模型。该模型提高了基于卡尔曼滤波器的GNSS导航精度。在城市驾驶中，车辆经常经历频繁的静止停车。为解决此问题，我们纳入零速约束，以便在车辆静止时进一步细化车辆位置数据。此约束将数据与适当的弹性滤波模型进行匹配。最终，我们进行了仿真和实际车辆导航实验，以证实我们所提出算法的有效性和合理性。与传统算法和现有的交互式多模型算法相比，所提出的算法显著提高了城市环境中车辆GNSS的导航和定位精度。与常用的恒加速度（CA）和匀速（CV）模型相比，定位精度有了显著提高。此外，与更先进的交互式多模型（IMM）模型相比，本文提出的方法在三个维度上分别将定位精度提高了21.8%、20.9%和31.3%。
