Xu Shuchen, Sun Yongrong, Zhao Kedong, Fu Xiyu, Wang Shuaishuai
National Key Laboratory of Helicopter Aeromechanics, College of Automation Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing 211106, China.
Sensors (Basel). 2023 Aug 31;23(17):7581. doi: 10.3390/s23177581.
Satellite signals are easily lost in urban areas, which causes difficulty in vehicles being located with high precision. Visual odometry has been increasingly applied in navigation systems to solve this problem. However, visual odometry relies on dead-reckoning technology, where a slight positioning error can accumulate over time, resulting in a catastrophic positioning error. Thus, this paper proposes a road-network-map-assisted vehicle positioning method based on the theory of pose graph optimization. This method takes the dead-reckoning result of visual odometry as the input and introduces constraints from the point-line form road network map to suppress the accumulated error and improve vehicle positioning accuracy. We design an optimization and prediction model, and the original trajectory of visual odometry is optimized to obtain the corrected trajectory by introducing constraints from map correction points. The vehicle positioning result at the next moment is predicted based on the latest output of the visual odometry and corrected trajectory. The experiments carried out on the KITTI and campus datasets demonstrate the superiority of the proposed method, which can provide stable and accurate vehicle position estimation in real-time, and has higher positioning accuracy than similar map-assisted methods.
卫星信号在城市地区很容易丢失，这导致车辆难以进行高精度定位。视觉里程计已越来越多地应用于导航系统以解决这一问题。然而，视觉里程计依赖于航位推算技术，在这种技术中，轻微的定位误差会随着时间积累，从而导致灾难性的定位误差。因此，本文基于位姿图优化理论提出了一种道路网络地图辅助车辆定位方法。该方法以视觉里程计的航位推算结果作为输入，并引入点线形式道路网络地图的约束来抑制累积误差，提高车辆定位精度。我们设计了一个优化和预测模型，通过引入地图校正点的约束对视觉里程计的原始轨迹进行优化，以获得校正后的轨迹。基于视觉里程计的最新输出和校正后的轨迹预测下一时刻的车辆定位结果。在KITTI和校园数据集上进行的实验证明了所提方法的优越性，该方法能够实时提供稳定、准确的车辆位置估计，并且比类似的地图辅助方法具有更高的定位精度。
