Abstract
Nowadays, intelligent systems applied to vehicles have grown very rapidly; their goal is not only the improvement of safety, but also making autonomous driving possible. Many of these intelligent systems are based on making use of computer vision in order to know the environment and act accordingly. It is of great importance to be able to estimate the pose of the vision system because the measurement matching between the perception system (pixels) and the vehicle environment (meters) depends on the relative position between the perception system and the environment. A new method of camera pose estimation for stereo systems is presented in this paper, whose main contribution regarding the state of the art on the subject is the estimation of the pitch angle without being affected by the roll angle. The validation of the self-calibration method is accomplished by comparing it with relevant methods of camera pose estimation, where a synthetic sequence is used in order to measure the continuous error with a ground truth. This validation is enriched by the experimental results of the method in real traffic environments.
Highlights
Nowadays, according to WHO (World Health Organization), traffic accidents are one of the main causes of death in the world, ranking ninth
This paper presents a new method of camera pose estimation for stereo systems, whose key feature is the fact that it allows one to estimate the pitch angle (θ) for high values of the roll angle (ρ) in relation to the ground
The vehicle turns to the right along the sequence, in such a way that this initial slope of the roll angle (ρ) is translated into a variation of the pitch angle (θ) while the vehicle is in motion, as we can see in the self-calibration results of the roll angle (ρ) (Figure 13d) and the pitch angle (θ) (Figure 13e)
Summary
Nowadays, according to WHO (World Health Organization), traffic accidents are one of the main causes of death in the world, ranking ninth. The number of deaths from this cause was approximately 25,700 people [2]. For this reason, there is a continuous social demand for improving road safety due to the high socio-economic cost of road accidents, being one of the major ones responsible for the deep development, that has taken place, in the implementation of security systems in everything related to the automotive industry, either by manufacturers, authorities or researchers in this field. Current examples of active safety systems based on computer vision are, among others: the blind spot monitor, traffic sign recognition or driver drowsiness detection
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