Abstract
The fusion of light detection and ranging (LiDAR) and camera data in real-time is known to be a crucial process in many applications, such as in autonomous driving, industrial automation, and robotics. Especially in the case of autonomous vehicles, the efficient fusion of data from these two types of sensors is important to enabling the depth of objects as well as the detection of objects at short and long distances. As both the sensors are capable of capturing the different attributes of the environment simultaneously, the integration of those attributes with an efficient fusion approach greatly benefits the reliable and consistent perception of the environment. This paper presents a method to estimate the distance (depth) between a self-driving car and other vehicles, objects, and signboards on its path using the accurate fusion approach. Based on the geometrical transformation and projection, low-level sensor fusion was performed between a camera and LiDAR using a 3D marker. Further, the fusion information is utilized to estimate the distance of objects detected by the RefineDet detector. Finally, the accuracy and performance of the sensor fusion and distance estimation approach were evaluated in terms of quantitative and qualitative analysis by considering real road and simulation environment scenarios. Thus the proposed low-level sensor fusion, based on the computational geometric transformation and projection for object distance estimation proves to be a promising solution for enabling reliable and consistent environment perception ability for autonomous vehicles.
Highlights
Many researchers have become interested in multi-sensor data convergence and data integration technologies based on sensors such as cameras, light detection and ranging (LiDAR), and radar as sensing technologies have developed for environment perception [1]
We propose a precise data fusion method between a camera and a LiDAR sensor to estimate object distance, which is necessary to enable a self-driving vehicle driving along a real road by sensing its surrounding environment more precisely
We focus on precise distance estimation as well as object detection in the path of an autonomous vehicle
Summary
Many researchers have become interested in multi-sensor data convergence and data integration technologies based on sensors such as cameras, light detection and ranging (LiDAR), and radar as sensing technologies have developed for environment perception [1]. Current state-of-the-art literature describes a highly accurate object detection system using vision and range sensing sensors, though there are still several issues to be solved in the context of autonomous driving. There are several approaches that have been proposed, depending on the modality of the sensors used, such as radar, LiDAR, or camera. Each sensor modality is capable of perceiving the environment with a specific perspective and is limited by detecting certain attribute information of objects. Vision-based approaches are more robust and accurate in object detection but fail in estimating the Symmetry 2020, 12, 324; doi:10.3390/sym12020324 www.mdpi.com/journal/symmetry
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