Title :
High-resolution terrain map from multiple sensor data
Author :
Kweon, In So ; Kanade, Takeo
Author_Institution :
Vision & Autonomous Syst. Center, Carnegie Mellon Univ., Pittsburgh, PA, USA
fDate :
2/1/1992 12:00:00 AM
Abstract :
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
Keywords :
computational geometry; computer vision; computerised navigation; computerised pattern recognition; mobile robots; 3D vision; computerised navigation; digital elevation map; locus method; mobile robots; multiple sensor data; robot vision; sensor geometry; synthetic range images; terrain map; terrain models; Geometry; Image sensors; Machine vision; Motion estimation; Navigation; Robot kinematics; Robot sensing systems; Robot vision systems; Shadow mapping; Vehicles;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on