基于深度学习的智能车辆视觉里程计技术发展综述

针对基于模型的视觉里程计在光照条件恶劣的情况下存在鲁棒性差、回环检测准确率低、动态场景中精度不够、无法对场景进行语义理解等问题,利用深度学习可以弥补其不足。首先,简略介绍了基于模型的里程计的研究现状,然后对比了常用的智能车数据集,将基于深度学习的视觉里程计分为有监督学习、无监督学习和模型法与深度学习结合3种,从网络结构、输入和输出特征、鲁棒性等方面进行分析,最后,讨论了基于深度学习的智能车辆视觉里程计研究热点,从视觉里程计在动态场景的鲁棒性优化、多传感器融合、场景语义分割3个方面对智能车辆视觉里程计技术的发展趋势进行了展望。

Review on the Development of Deep Learning-Based Vision Odometer Technologies for Intelligent Vehicles

Visual odometer can,achieve with deep learning,better performance on robustness and accuracy through solving the problems such as the weak robustness under poor illumination,low detection accuracy in close loop and insufficient accuracy in dynamic scenarios,disability in understanding the scenario semantically.Firstly,this paper briefly introduces the research status of the model-based odometer,then compares the commonly-used intelligent vehicle datasets,and then divides the learning-based visual odometer into supervised learning,unsupervised learning and hybrid model which combines model-based with deep learning-based model.Furthermore,it analyzes the learning-based visual odometer from the aspects of network structure,input and output characteristics,robustness and so on.Finally,the research hotspots of learning-based visual odometer for intelligent vehicle are discussed.The development trend of learning-based visual odometer for intelligent vehicle is discussed from 3 aspects which respectively are robustness in dynamic scenarios,multisensor fusion,and scenario semantic segmentation.