Paper Info
Title
Performance of global descriptors for velodyne-based urban object recognition
Abstract
Object Recognition is an essential component for Autonomous Land Vehicle (ALV) navigation in urban environments. This paper presents a thorough evaluation of the performance of some state of the art global descriptors on the public Sydney Urban Objects Dataset1, which was collected in the Central Business District of Sydney. These descriptors are Bounding Box descriptor, Histogram of Local Point Level descriptor, Hierarchy descriptor, and Spin Image (SI). We also propose a novel Global Fourier Histogram (GFH) descriptor. Experimental results on the public data set show that GFH descriptor turns out to be one of the best global descriptors for the object recognition in urban environments, and the results on the data collected by our own ALV in urban environments also demonstrate its usefulness.
Year
DOI
Venue
2014
10.1109/IVS.2014.6856425
Intelligent Vehicles Symposium
Keywords
Field
DocType
gfh descriptor,velodyne-based urban object recognition,spin image,global descriptors,central business district,statistical analysis,si,sydney urban objects dataset,mobile robots,alv navigation,australia,path planning,urban environment,histogram-of-local point level descriptor,object recognition,autonomous land vehicle,global fourier histogram,bounding box descriptor,hierarchy descriptor,remotely operated vehicles,robot vision,accuracy,azimuth,silicon,histograms
Motion planning,Histogram,Remotely operated underwater vehicle,Computer vision,Computer science,Azimuth,Central business district,Artificial intelligence,Mobile robot,Cognitive neuroscience of visual object recognition,Statistical analysis
Conference
ISSN
Citations 
PageRank 
1931-0587
8
0.50
References 
Authors
15
4
Name
Order
Citations
PageRank
Tongtong Chen1616.88
Bin Dai2699.23
Daxue Liu311610.89
Jinze Song4395.26