Robust Horizon Finding Algorithm for Real-Time Autonomous Navigation
Total Page：16
File Type：pdf, Size：1020Kb
Robust Horizon Finding Algorithm for Real-Time Autonomous Navigation based on Monocular Vision Arthur de Miranda Neto, Alessandro Correa Victorino, Isabelle Fantoni, Douglas Eduardo Zampieri To cite this version: Arthur de Miranda Neto, Alessandro Correa Victorino, Isabelle Fantoni, Douglas Eduardo Zampieri. Robust Horizon Finding Algorithm for Real-Time Autonomous Navigation based on Monocular Vision. 14th IEEE International Conference on Intelligent Transportation Systems (ITSC 2011), Oct 2011, Washington DC, United States. pp.532-537. hal-00657425 HAL Id: hal-00657425 https://hal.archives-ouvertes.fr/hal-00657425 Submitted on 6 Jan 2012 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Robust Horizon Finding Algorithm for Real-Time Autonomous Navigation based on Monocular Vision A. Miranda Neto, A. Corrêa Victorino, I. Fantoni and D. E. Zampieri Abstract —Navigation of an Autonomous Vehicle is based techniques [8]. Besides their intrinsic higher computational on its interaction with the environment, through information complexity caused by a significant increment in the acquired by sensors. The perception of the environment is a amount of data to be processed, the most common vision major issue in autonomous and (semi)-autonomous systems. systems (such as the processing of non-monocular images) This work presents the embedded real-time visual perception must also be robust enough to tolerate noise caused by problem applied to experimental platform. In this way, a robust horizon finding algorithm that finds the horizon line platform movements and drifts in the calibration of the was proposed and applied to generate the navigable area. It multiple cameras’ setup. permits to investigate dynamically only a small portion of the For land vehicle navigation, the monocular vision image (road) ahead of the vehicle. From a dynamic threshold systems have been designed to investigate the road search method based on Otsu segmentation and Hough information, and in order to decrease the volume of data transform, this system was robust to illumination changes and does not need any contrast adjustments. It can also be used for processing, some systems have been designed to with infrared camera. investigate only a small portion of the road ahead of the vehicle where the absence of other vehicles has been I. INTRODUCTION assumed [8]. Otherwise, the sky region is not a region of HE robot navigation is based on its interaction with the interest, and the horizon line threshold is applied to Tenvironment. In order to obtain information about it, generate a road image [9]. Stanford Racing Team [10] sensors are needed, which in many cases may be limited in implemented the horizon finding algorithm originally scope and subject to noise. However, when incorporating proposed by [11] to eliminate all pixels above that horizon. several types of sensors, there is an increase of autonomy Additionally, these techniques has been used for flight and “intelligence” degrees, especially in relation to stability and control system to Micro Air Vehicles [11], for navigation in unknown environments. On the other hand, the control of the airship [12] and landing aircraft control the type and quantity of sensors determine the volume of [13]. It also was employed as an absolute attitude sensor data for processing that requires a high computational cost. which is useful for low-level control of an unmanned aerial Furthermore, the important factors are the variety and vehicle [14]. complexity of environments and situations. For military or In this way, from a dynamic threshold search method civil purposes, some of these applications include: the based on Otsu segmentation [15] and Hough transform [16], a robust horizon finding algorithm that finds the Grand Challenge [1] and Urban Challenge [2]; Advanced horizon line was proposed and applied to generate the Driver Assistance Systems (ADAS) [3]; autonomous navigable area. It permits to investigate dynamically only a perception system [4], [5], and aerial robots [6], [7]. These small portion of the image (road) ahead of the vehicle. applications have a common issue: providing to the This algorithm is robust to illumination changes and does robot/vehicle platform the capability of perceiving and not need any contrast adjustments. interacting with its neighbour environment. The Section II presents the sensor perception review and Although extremely complex and highly demanding, monocular vision contributions. From the Section III and thanks to the great deal of information it can deliver, IV the results are presented, and the conclusions are given machine vision is a powerful means for sensing the in Section V. environment and has been widely employed to deal with a large number of tasks in the automotive field [8]. II. SENSOR PERCEPTION Due to the general applicability of it, the problem of Several models of distributed vehicle navigation mobile robots navigation is dealt with using more complex architecture are presented in the literature [1], [2], [17]. The perception layer uses many types of sensors, including Manuscript received July 13, 2011. ultrasonic sensors, laser rangefinders, radar, cameras, etc. Arthur de Miranda Neto is Ph.D. Student in Mechanical Engineering at the State University of Campinas – Brazil and in Information However, these sensors are not perfect: ultrasonic Technology and Systems at the University of Technology of Compiègne sensors are cheap but suffer from specular reflections, and – CNRS UMR 6599, Heudiasyc, France. Actually, he is researcher laser rangefinders and radar provide better resolution but engineer at the CNRS. are more complex and more expensive [4]. Alessandro Corrêa Victorino is associate professor at the University of Technology of Compiègne – CNRS UMR 6599, Heudiasyc, France. According to [8], the vision-based sensors are defined as Isabelle Fantoni is researcher at the University of Technology of passive sensors, the image scanning is performed fast Compiègne – CNRS UMR 6599, Heudiasyc, France. enough for Intelligent Transportation Systems, and it can Douglas Eduardo Zampieri is associate professor of the Computational Mechanics Department, at the State University of be used for some specific applications: road marking Campinas, Brazil. localization, traffic signs recognition, obstacle operations on images, with the purpose to reduce noise and identification. However, vision sensors are less robust than to segment them. millimeter-wave radars in foggy, night, or direct sun-shine One way to perform segmentation of an image is to use conditions. thresholds. This type of segmentation technique, called On the other hand, for range-based obstacle detection thresholding, is very simple and computationally fast, systems have difficulty for detecting small or flat objects however the identification of the ideal threshold can be on the ground, and range sensors are also unable to sufficiently complicated. The best thing to do in this case distinguish between different types of ground surfaces [4]. is to use techniques and algorithms that search the Notwithstanding, the main problem with the use of active thresholds automatically. Thresholding methods are sensors is represented by interference among sensors of the divided in two groups: global and local. The global ones same type, hence, foreseeing a massive and widespread use divide the image using only one threshold and the local of these sensing agents, the use of passive sensors obtains ones are those that divide the image in sub-images and for key advantages [8]. each one of them a threshold is defined [19]. Fig. 1 illustrates the monocular vision contribution to What this work proposes is a global thresholding the DARPA Grand Challenge [10]. This diagram shows method, which seeks not the ideal threshold for the whole that the reach of lasers was approximately 22 meters (left), image, but an ideal threshold associated at each portion whereas the monocular vision module often looks 70 (local information), that contributes to the final decision. meters ahead (right). It also shows the horizon detection Details of this search are described below. for sky removal. A. Image pre-processing Most research groups face this problem using highly sophisticated image filtering algorithms [8]. This work uses a color or gray-level image and smooth them using a Gaussian filter. The Gaussian smoothing operator is a 2-D convolution operator. It acts as low-pass frequency filters [20]. In order to reduce the number of data, it includes the resolution reduction of image (to 128x96). If the image is colored, in order to utilize the most Fig. 1 – Monocular vision: comparison of the laser-based important information of the color image, the candidate (left) and the image-based (right) mapper. Circles are color channel that was dominant in certain color space is spaced around the vehicle at 10 meters distance. It also selected to generate the histogram image [21]. For us this shows the horizon detection for sky removal [10]. space represents 60% of the image (Fig. 5). It is described by Eq. (1). On the safety front, the ADAS including front bumper mounted cameras. The progressive safety systems will be (1) Cc = arg max (N R , NG , N B ) developed through the manufacturing of an “intelligent {}R,G,B bumper” (safety belt) peripheral to the vehicle in answering new features as: lane change assistant, blind where Cc means the color channel and N means the spot detection, frontal and lateral pre-crash, etc. The R objective in terms of cost to fill ADAS functions has to be number of the dominant color channel in certain very lower than the current Adaptive Cruise Control (500 referenced region. Euros) [18].