A trinocular stereo system for highway obstacle detection

Williamson, Todd; Thorpe, C.

doi:10.1109/robot.1999.770443

Cited by 27 publications

(13 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Equation (14) in the previous subsection presents the equation of this function for a right camera. In the case of four pairs of cameras, this function is defined on the basis of the single pair version.…”

Section: Msa Algorithm For the Set Of Five Camerasmentioning

confidence: 99%

Improving depth maps of plants by using a set of five cameras

Kaczmarek

2015

J. Electron. Imaging

View full text Add to dashboard Cite

Abstract. Obtaining high-quality depth maps and disparity maps with the use of a stereo camera is a challenging task for some kinds of objects. The quality of these maps can be improved by taking advantage of a larger number of cameras. The research on the usage of a set of five cameras to obtain disparity maps is presented. The set consists of a central camera and four side cameras. An algorithm for making disparity maps called multiple similar areas (MSA) is introduced. The algorithm was specially designed for the set of five cameras. Experiments were performed with the MSA algorithm and the stereo matching algorithm based on the sum of sum of squared differences (sum of SSD, SSSD) measure. Moreover, the following measures were included in the experiments: sum of absolute differences (SAD), zero-mean SAD (ZSAD), zero-mean SSD (ZSSD), locally scaled SAD (LSAD), locally scaled SSD (LSSD), normalized cross correlation (NCC), and zero-mean NCC (ZNCC). Algorithms presented were applied to images of plants. Making depth maps of plants is difficult because parts of leaves are similar to each other. The potential usability of the described algorithms is especially high in agricultural applications such as robotic fruit harvesting.

show abstract

Section: Msa Algorithm For the Set Of Five Camerasmentioning

confidence: 99%

Improving depth maps of plants by using a set of five cameras

Kaczmarek

2015

J. Electron. Imaging

View full text Add to dashboard Cite

show abstract

“…Robot driving has concentrated on forward-looking sensing, for road following and obstacle detection [21,22,23,24,25,26,27]. This is an appropriate first step, but real deployment of mobile robots will require additional sensing and reasoning to surround the robot with safeguard sensors and systems.…”

Section: The Need For 360 Degree Safeguardingmentioning

confidence: 99%

Perception for collision avoidance and autonomous driving

et al. 2003

View full text Add to dashboard Cite

The Navlab group at Carnegie Mellon University has a long history of development of automated vehicles and intelligent systems for driver assistance. The earlier work of the group concentrated on road following, cross-country driving, and obstacle detection. The new focus is on short-range sensing, to look all around the vehicle for safe driving. The current system uses video sensing, laser rangefinders, a novel light-stripe rangefinder, software to process each sensor individually, a map-based fusion system, and a probability based predictive model. The complete system has been demonstrated on the Navlab 11 vehicle for monitoring the environment of a vehicle driving through a cluttered urban environment, detecting and tracking fixed objects, moving objects, pedestrians, curbs, and roads. Robotics Technology and Intelligent VehiclesDuring the decade of the 1990's, researchers from automotive engineering, from robotics, and from allied fields came together to start building intelligent vehicles [1,2,3,4]. These vehicles use sensors, actuators, processors, and communications systems to either drive automatically, or to monitor a human driver and assist in navigation or warn in case of a developing dangerous situation.The field was driven by several market demand factors. First is an increasing interest in safety. While the accident rate has decreased steadily for 40 years, the total miles traveled have increased, with the net result that the number of road fatalities in the US has stayed approximately constant, averaging 40,000 / year [5]. Furthermore, there is an increasing sense that vehicles have become safer (air bags, anti-lock brakes, crumple zones); roadways have become safer (the Interstate highway system); societal pressures have become stronger (mandatory child safety seats, serious enforcement of drunk driving laws) but the drivers themselves have not necessarily improved. Currently, in the US, 70% of all crashes are primarily caused by the drivers, and in another 20% driver error is a major contributing factor [5,6].Beyond safety, the next major driving factors have to do with congestion and pollution. In the US, traffic continues to grow at 4% / year, while with the completion of the Interstate highway system, major new road construction greatly lags traffic growth. Computercontrolled vehicles promise to smooth out traffic flow, permitting more vehicles to travel in

show abstract

“…However, these applications are limited to the existence detection of the obstacle, and rarely the position measurement is contained. And as the set of range data or disparity data, the disparity image may be the low resolution one such that its width and height are in the extent of several decade pixels, and the judgment of obstacle may be done directly based on individual pixel and its transformation to the point in 3D space [2].…”

Section: Related Researchesmentioning

confidence: 99%

Hybrid machine vision method for autonomous guided vehicles

Hamajima

Ishihara

2003

Machine Vision Applications in Industrial Inspection XI

View full text Add to dashboard Cite

As a prospective intelligent sensing method for Autonomous Guided Vehicle (AGV), machine vision is expected to have balanced ability of covering a large space and also recognizing details of important objects. For this purpose, the proposed hybrid machine method here combines the stereo vision method and the traditional 2D method. The former implements coarse recognition to extract object over a large space, and the later implement fine recognition about some sub-areas corresponding to important and/or special objects. This paper is mainly about the coarse recognition. In order to extract objects in the coarse recognition stage, the disparity image calculated according to stereo vision principle is segmented by two consequent steps of region expansion and convex split. Then the 3D measurement about the rough positions and sizes of extracted objects is performed according to the disparity information of the corresponding segmentation, and is used for recognizing the objects' attributes by means of pattern learning/recognition. The attribute information resulted is further used to assist fine recognition in the way of performing gaze control to input suitable image of the interested objects, or to directly control AGV's travel. In our example AGV application, some navigation-signs are introduced to indicate the travel route. When the attribute shows that the object is a navigationsign, the 3D measurement is used to gaze the navigation-sign, in order for the fine recognition to analyze the specific meaning by means of traditional 2D method.

show abstract

A trinocular stereo system for highway obstacle detection

Cited by 27 publications

References 8 publications

Improving depth maps of plants by using a set of five cameras

Improving depth maps of plants by using a set of five cameras

Perception for collision avoidance and autonomous driving

Hybrid machine vision method for autonomous guided vehicles

Contact Info

Product

Resources

About