A discriminating feature tracker for vision-based autonomous driving

Schneiderman, Henry; Nashman, Marilyn

doi:10.1109/70.338531

Cited by 40 publications

(9 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Servic and Ribaric (2001), perspective lines are used to find the absolute orientation within the corridor. Schneiderman and Nashman (1994) develop a technique to detect and approximate lane markers by using a second-order polynomial in the image plane. No control algorithm is proposed in the paper.…”

Section: Introductionmentioning

confidence: 99%

Control based on perspective lines of a non-holonomic mobile robot with camera-on-board

Carelli

Kelly

Nasisi

et al. 2006

International Journal of Control

View full text Add to dashboard Cite

This paper addresses the control of wheeled mobile robots based on visual information of perspective lines. These lines can be generated by the projection of environment lines like those formed by the intersection of walls and floor, the structural features delimiting paths, or by painted lines on the floor. The control objective is to navigate at a desired speed along the lines, keeping a specified separating distance from them. No assumption is made on the absolute location of either the robot or the lines. A smooth time-invariant controller is proposed in order to command the angular velocity of the robot, while the linear velocity is generated to achieve a cautious and smooth motion. By invoking the LaSalle's invariance principle convergence is shown to a proper invariant set which guarantees accomplishment of the control objective. Finally, experimental results illustrating the performance of the control system are presented.

show abstract

Section: Introductionmentioning

confidence: 99%

Control based on perspective lines of a non-holonomic mobile robot with camera-on-board

Carelli

Kelly

Nasisi

et al. 2006

International Journal of Control

View full text Add to dashboard Cite

show abstract

“…• Unstructured roads • Unclear road edges • Nonhomogeneous road appearance • Arbitrary road shape • Poor, inconsistent lighting conditions (e.g., shadows) • Paved and unpaved roads One class of road following algorithms focuses on the detection of highway lane markings (Dickmanns [8], Thorpe [22], Schneiderman [21] Rotaru et al, [20], Beucher [4], and Kosecks [15]). These algorithms are fast and well-suited for the task of highway driving.…”

Section: Introductionmentioning

confidence: 99%

Color model-based real-time learning for road following

Tan

Hong

Chang

et al. 2006

2006 IEEE Intelligent Transportation Systems Conference

View full text Add to dashboard Cite

Road following is an important skill vital to the development and deployment of autonomous vehicles. Over the past few decades, a large number of road following computer vision systems have been developed. All of these systems have limitations in their capabilities, arising from assumptions of idealized conditions. The systems show dependency on highly structured roads, road homogeneity, simplified road shapes, and idealized lighting conditions. In the real world, the systems are only effective in specialized cases. This paper proposes a vision system that is capable of dealing with many of these limitations, accurately segmenting unstructured, nonhomogeneous roads of arbitrary shape under various lighting conditions. The system uses color classification and learning to construct and use multiple road and background models. Color models are constructed on a frame by frame basis and used to segment each color image into road and background by estimating the probability that a pixel belongs to a particular model. The models are constructed and learned independently of road shape, allowing the segmentation of arbitrary road shapes. Temporal fusion is used in the stabilization of the results. Preliminary testing demonstrates the system's effectiveness on roads not handled by previous systems.

show abstract

“…Blue points are road edges, green points show the model fitted to the boundary 3 SENSORY PROCESSING FOR ROAD AND LANE TRACKING3.1 Lane tracking algorithmThe algorithm used for lane tracking is similar to that described in Schneiderman and Nashman17 . The stages of the algorithm involve first predicting the locations of lane markers, then extracting and classifying edges, and, finally, updating the lane model.…”

mentioning

confidence: 99%

<title>Road detection and tracking for autonomous mobile robots</title>

et al. 2002

View full text Add to dashboard Cite

As part of the Army's Demo III project, a sensor-based system has been developed to identify roads and to enable a mobile robot to drive along them. A ladar sensor, which produces range images, and a color camera are used in conjunction to locate the road surface and its boundaries. Sensing is used to constantly update an internal world model of the road surface. The world model is used to predict the future position of the road and to focus the attention of the sensors on the relevant regions in their respective images. The world model also determines the most suitable algorithm for locating and tracking road features in the images based on the current task and sensing information. The planner uses information from the world model to determine the best path for the vehicle along the road. Several different algorithms have been developed and tested on a diverse set of road sequences. The road types include some paved roads with lanes, but most of the sequences are of unpaved roads, including dirt and gravel roads. The algorithms compute various features of the road images including smoothness in the world model map and in the range domain, and color features and texture in the color domain. Performance in road detection and tracking are described and examples are shown of the system in action.

show abstract

A discriminating feature tracker for vision-based autonomous driving

Cited by 40 publications

References 9 publications

Control based on perspective lines of a non-holonomic mobile robot with camera-on-board

Control based on perspective lines of a non-holonomic mobile robot with camera-on-board

Color model-based real-time learning for road following

<title>Road detection and tracking for autonomous mobile robots</title>

Contact Info

Product

Resources

About