Computing object-based saliency in urban scenes using laser sensing

It becomes a well-known technology that a low-level map of complex environment containing 3D laser points can be generated using a robot with laser scanners. Given a cloud of 3D laser points of an urban scene, this paper proposes a method for locating the objects of interest, e. g. traffic signs or road lamps, by computing object-based saliency. Our major contributions are: 1) a method for extracting simple geometric features from laser data is developed, where both range images and 3D laser points are analyzed; 2) an object is modeled as a graph used to describe the composition of geometric features; 3) a graph matching based method is developed to locate the objects of interest on laser data. Experimental results on real laser data depicting urban scenes are presented; efficiency as well as limitations of the method are discussed.

Authors

• Yipu Zhao
• Mengwen He
• Huijing Zhao
• Franck Davoine
• Hongbin Zha

Keywords

• Feature extraction
• Vectors
• Sensors
• Laser modes
• Merging
• Measurement by laser beam
• Urban Scenes
• Laser Scanning
• Geometric Features
• Light Signal
• Object Of Interest
• Standard Light
• 3D Point
• Laser Pointer
• Graph Matching
• Solid Line
• Horizontal Plane
• Graphical Representation
• Vertical Line
• Urban Environments
• Object Detection
• Regional Growth
• Vertical Plane
• Markov Random Field
• 3D Point Cloud
• Objects In The Scene
• Saliency Map
• Object Coordinates
• Principal Directions
• Geometric Primitives
• Valid Pixels
• Potential Objects
• Salient Object
• Man-made Objects
• Hough Transform
• Neighboring Data