Estimating and understanding the surroundings of the vehicle precisely forms the basic and crucial step for the autonomous vehicle. The perception system plays a significant role in providing an accurate interpretation of a vehicle's environment in real-time. Generally, the perception system involves various subsystems such as localization, obstacle (static and dynamic) detection, and avoidance, mapping systems, and others. For perceiving the environment, these vehicles will be equipped with various exteroceptive (both passive and active) sensors in particular cameras, Radars, LiDARs, and others. These systems are equipped with deep learning techniques that transform the huge amount of data from the sensors into semantic information on which the object detection and localization tasks are performed. For numerous driving tasks, to provide accurate results, the location and depth information of a particular object is necessary. 3D object detection methods, by utilizing the additional pose data from the sensors such as LiDARs, stereo cameras, provides information on the size and location of the object. Based on recent research, 3D object detection frameworks performing object detection and localization on LiDAR data and sensor fusion techniques show significant improvement in their performance. In this work, a comparative study of the effect of using LiDAR data for object detection frameworks and the performance improvement seen by using sensor fusion techniques are performed. Along with discussing various state-of-the-art methods in both the cases, performing experimental analysis, and providing future research directions.
翻译:感知系统在实时准确解释车辆环境方面起着重要作用。一般而言,感知系统涉及多个子系统,如定位、障碍(静态和动态)探测和避免、绘图系统等。为了解环境,这些飞行器将配备各种外向(被动和主动)传感器(包括被动和主动)传感器,特别是照相机、雷达、激光雷达、激光雷达等。这些系统配备了深层学习技术,将传感器上的大量数据转换成可执行物体探测和定位任务的语义信息。对于许多驱动任务来说,提供准确的结果、特定物体的位置和深度信息是必要的。3D物体探测方法,利用LIDARs、立体摄像机等传感器提供的额外构成数据,提供关于物体大小和位置的信息。根据最近的研究,3D物体探测框架,对激光雷达数据和感应变技术进行天体探测和定位,通过开展比较性能分析,通过开展比较性能分析,通过开展比较性能分析,对各种实验性能进行分析。