Terrain classification based on structure for autonomous navigation in complex environments

One of the main challenges for autonomous navigation in cluttered outdoor environments is to determine which obstacles can be driven over and which need to be avoided. Especially in off-road driving, the aim is not only to recognize the lethal obstacles on the vehicle's way at all costs, but also to predict the scene category thereby giving a better decision-making framework for vehicle navigation. This paper studies terrain classification based on structure relying on sparse 3-D data from LADAR mobility sensors. While most of recent methods for LADAR processing are purely found on the local point density and spatial distribution of the 3-D point cloud directly. We, on the other hand, introduce a new approach to analyze the point cloud by considering local properties and distance variation of pixels inside edgeless areas. First of all, the edgeless areas are extracted from segmenting the 3-D point cloud into homogeneous regions by Graph-Cut technique. Secondly, the neighbor distance variation inside edgeless areas (NDVIE) features are obtained by calculating the euclidean distance of neighbor distance variation inside each region. Through extensive experiments, we demonstrate that this feature has properties complementary to the conditional local point statistics features traditionally used for point cloud analysis, and show significant improvement in classification performance for tasks relevant to outdoor navigation.

[1]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[2]  Guoqiang Peter Zhang,et al.  Neural networks for classification: a survey , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[3]  Martial Hebert,et al.  Natural terrain classification using three‐dimensional ladar data for ground robot mobility , 2006, J. Field Robotics.

[4]  David Mumford,et al.  Statistics of range images , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[5]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[6]  Christopher Rasmussen,et al.  Combining laser range, color, and texture cues for autonomous road following , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[7]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[8]  Markus Ax,et al.  Combining Distance and Modulation Information for Detecting Pedestrians in Outdoor Environment using a PMD Camera , 2010 .

[9]  Ben Taskar,et al.  Discriminative learning of Markov random fields for segmentation of 3D scan data , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[10]  Daniel P. Huttenlocher,et al.  Efficient Graph-Based Image Segmentation , 2004, International Journal of Computer Vision.

[11]  Richard Willstätter,et al.  Untersuchungen Uber Chlorophyll: Methoden Und Ergebnisse , 2009 .

[12]  David M. Bradley,et al.  Vegetation Detection for Driving in Complex Environments , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[13]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[14]  Jeff A. Bilmes,et al.  A gentle tutorial of the em algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models , 1998 .

[15]  G. Baudat,et al.  Kernel-based methods and function approximation , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[16]  Martial Hebert,et al.  Natural terrain classification using 3-d ladar data , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.