Title :
Object recognition in 3D lidar data with recurrent neural network
Author :
Prokhorov, Danil V.
Author_Institution :
TTC-TEMA, Toyota Res. Inst. NA, Ann Arbor, MI, USA
Abstract :
This paper introduces a new method for object recognition which is based on a recurrent neural network trained in a supervised mode. The RNN inputs 3-dimensional laser scanner data sequentially, in a natural temporal order in which the laser returns arrive to the scanner. The method is illustrated on a two-class problem with real data.
Keywords :
learning (artificial intelligence); object recognition; optical radar; recurrent neural nets; 3D lidar data; object recognition; recurrent neural network; supervised learning; Cameras; Clouds; Distance measurement; Hardware; Laser modes; Laser radar; Object recognition; Recurrent neural networks; Remotely operated vehicles; Turning;
Conference_Titel :
Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009. IEEE Computer Society Conference on
Conference_Location :
Miami, FL
Print_ISBN :
978-1-4244-3994-2
DOI :
10.1109/CVPRW.2009.5204114
