Vision for Mobile Robot Navigation: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
AI Game Programming Wisdom
Neural Network Perception for Mobile Robot Guidance
Neural Network Perception for Mobile Robot Guidance
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Appearance-Based Obstacle Detection with Monocular Color Vision
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Machine Vision: Theory, Algorithms, Practicalities
Machine Vision: Theory, Algorithms, Practicalities
High speed obstacle avoidance using monocular vision and reinforcement learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
On-Road Vehicle Detection: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vision-based road detection in automotive systems: a real-time expectation-driven approach
Journal of Artificial Intelligence Research
Color-based road detection in urban traffic scenes
IEEE Transactions on Intelligent Transportation Systems
GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection
IEEE Transactions on Image Processing
On the error analysis of vertical line pair-based monocular visual odometry in urban area
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Visual perception-based motion planning using road map
International Journal of Computational Vision and Robotics
Neural-swarm visual saliency for path following
Applied Soft Computing
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We report our development of a vision-based motion planning system for an autonomous motorcycle designed for desert terrain, where uniform road surface and lane markings are not present. The motion planning is based on a vision vector space (V2-Space), which is a unitary vector set that represents local collision-free directions in the image coordinate system. The V2-Space is constructed by extracting the vectors based on the similarity of adjacent pixels, which captures both the color information and the directional information from prior vehicle tire tracks and pedestrian footsteps. We report how the V2-Space is constructed to reduce the impact of varying lighting conditions in outdoor environments. We also show how the V2-Space can be used to incorporate vehicle kinematic, dynamic, and time-delay constraints in motion planning to fit the highly dynamic requirements of the motorcycle. The combined algorithm of the V2-Space construction and the motion planning runs in O(n) time, where n is the number of pixels in the captured image. Experiments show that our algorithm outputs correct robot motion commands more than 90% of the time.