Visual reconstruction
Papers from the second workshop Vol. 13 on Human and Machine Vision II
Knowledge-based vision technology overview for obstacle detection and avoidance
Proceedings of a workshop on Image understanding workshop
Modeling rugged terrain by mobile robots with multiple sensors
Modeling rugged terrain by mobile robots with multiple sensors
Bayesian modeling of uncertainty in low-level vision
Bayesian modeling of uncertainty in low-level vision
Mars Rover Autonomous Navigation
Autonomous Robots
Visual terrain mapping for Mars exploration
Computer Vision and Image Understanding
Real-time motion planning of an autonomous mobile manipulator using a fuzzy adaptive Kalman filter
Robotics and Autonomous Systems
Gaussian process modeling of large scale terrain
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Interested sample point pre-selection based dense terrain reconstruction for autonomous navigation
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Space-carving Kernels for Accurate Rough Terrain Estimation
International Journal of Robotics Research
Wide-baseline stereo vision for terrain mapping
Machine Vision and Applications - Integrated Imaging and Vision Techniques for Industrial Inspection
Stereo-vision-based navigation of a six-legged walking robot in unknown rough terrain
International Journal of Robotics Research
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Terrain traversability analysis methods for unmanned ground vehicles: A survey
Engineering Applications of Artificial Intelligence
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The authors present 3-D vision techniques for incrementally building an accurate 3-D representation of rugged terrain using multiple sensors. They have developed the locus method to model the rugged terrain. The locus method exploits sensor geometry to efficiently build a terrain representation from multiple sensor data. The locus method is used to estimate the vehicle position in the digital elevation map (DEM) by matching a sequence of range images with the DEM. Experimental results from large-scale real and synthetic terrains demonstrate the feasibility and power of the 3-D mapping techniques for rugged terrain. In real world experiments, a composite terrain map was built by merging 125 real range images. Using synthetic range images, a composite map of 150 m was produced from 159 images. With the proposed system, mobile robots operating in rugged environments can build accurate terrain models from multiple sensor data.