Generalized predictive control—Part I. The basic algorithm
Automatica (Journal of IFAC)
A Three-Frame Algorithm for Estimating Two-Component Image Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape and motion from image streams: a factorization method
Shape and motion from image streams: a factorization method
High performance computing for vision on distributed-memory machines
High performance computing for vision on distributed-memory machines
Robot Vision
Artificial Vision for Mobile Robots: Stereo Vision and Multisensory Perception
Artificial Vision for Mobile Robots: Stereo Vision and Multisensory Perception
Multi-Primitive Hierarchical (MPH) Stereo Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Projective Structure from Uncalibrated Images: Structure From Motion and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual Navigation for Mobile Robots: A Survey
Journal of Intelligent and Robotic Systems
Unmanned Aerial Vehicle Localization Based on Monocular Vision and Online Mosaicking
Journal of Intelligent and Robotic Systems
An Unmanned Aircraft System for Automatic Forest Fire Monitoring and Measurement
Journal of Intelligent and Robotic Systems
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This paper describes an integrated vehicle control system with visual feedback. A general-purpose, low-level feature matching method, able to work in real time without any strict assumptions on the environment structure or camera parameters, generates low-level matching results, which are used as source of data for applications like mobile object tracking, among others. A generalized predictive path-tracking control approach keeps the vehicle on the trajectory defined by the moving target. In the low-level matching process, block-based features (windows) are selected and tracked along a stream of monocular images; least residual square error and similarity between clusters of features are used as constraints to select the right matching pair between multiple candidates. Real-time performance is achieved through optimized algorithms and a parallel DSP-based multiprocessor system implementation. Object detection and tracking is motion-based, and does not require a predefined model of the target. The integrated control system has been tested on the ROMEO-3R experimental vehicle.