Abstract
In the obstacle detection system, a great challenge is the perception of the surrounding environment due to the inherent limitation of the sensor. In this paper, a novel fusion methodology is proposed, which can effectively improve the accuracy of obstacle detection compared with the vision-based system and laser sensor system. This fusion methodology builds a sport model based on the type of obstacle and adopts a decentralized Kalman filter with a two-layer structure to fuse the information of LiDAR and vision sensor. We also put forward a new obstacles-tracking strategy to match the new detection with the previous one. We conducted a series of simulation experiments to calculate the performance of our algorithm and compared it with other algorithms. The results show that our algorithm has no obvious advantage when all the sensors are faultless. However, if some sensors fail, our algorithm can evidently outperform others, which can prove the effectiveness of our algorithm with higher accuracy and robustness.
Highlights
The development of sensor technology could drive the development of unmanned system technology to some extent, because the unmanned system's perception of the surrounding environment is based on sensor information [2]
Compared with single sensor information, multi-sensor data fusion appears more competitive in the following aspects: fault tolerance, complementarity, real-time and economy [5], so it has been gradually applied
We tested in a simulated environment to examine the feasibility of single-sensor system and the fusion system independently
Summary
The sensor is a component or device that can sense surrounding environment information and can convert these messages into corresponding useful output signals according to certain rules [1]. The development of sensor technology could drive the development of unmanned system technology to some extent, because the unmanned system's perception of the surrounding environment is based on sensor information [2]. Because of the inherent limitations of sensors (such as the low precision of the ultrasonic sensor [3], the insensitivity of the laser sensor to the transparent object and the failure of the visual sensor in the dark environment, etc.), single sensor system cannot achieve high accuracy in obstacle detection. Compared with single sensor information, multi-sensor data fusion appears more competitive in the following aspects: fault tolerance, complementarity, real-time and economy [5], so it has been gradually applied. Fernando García et al [7] proposed a high-level fusion scheme that makes it possible to improve the classical ADAS (advanced driving assistance) system by integrating data provided by laser sensors and computer vision
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