Learning Algorithms for Terrain Analysis

1. Learning Algorithms for Terrain Analysis Jackie Soenneker

2. The Scenario • Robot uses its LADAR to scan an area • “Features” are extracted from the scan data • Features are passed to a terrain classifier • Terrain classifier returns the type of terrain in the scan

3. The Terrain Classifier • My part: the terrain classifier • The terrain classifier will use a learning algorithm • Recommended learning algorithm: a Neural Net

4. Learning Methods • Neural Networks • Consist of an input layer, output layer, and hidden layer(s) • Input values feed through the network, influenced by connection weights • Network learns by adjusting the connection weights

5. Learning Methods con. • Maximum Likelihood Classifier • An input is labeled as an element of the class that it most likely belongs to • Class likelihoods are based upon a Probability Density Function (PDF; represents the probability distribution), which is learned from training data • Bayesian Classifier • Basically the same as the ML classifier

6. Research Sample Paper Learning Method(s) Task Data Source * See last slide for paper titles, etc.

7. Justification for Using NNs • Both ML and Bayesian classifiers need a PDF; NNs do not • Several papers compared performance of NNs and ML classifiers; NNs usually did better • NNs are good at dealing with noisy data and generalizing to new situations

8. NN for Our Project Inputs Hidden Layer(s) Outputs height slope grass mean concrete standard deviation trees distribution coefficient weighted connections

9. Conclusions • Nobody else is doing exactly what we are • Neural Nets and Maximum Likelihood and Bayesian classifiers are most popular learning methods for terrain analysis • Neural Nets have characteristics that may be helpful in our project • I recommend that we use a Neural Net!

10. Table Paper Key • 1 – Decatur, Scott Evan, “Application of Neural Networks to Terrain Classification,” in Proceedings of the IEEE International Joint Conference on Neural Networks, pp. 283-288, June 1989. • 2 – P. Jansen, W. van der Mark, J.C. van den Heuvel, and F.C.A. Groen, “Colour based Off-Road Environment and Terrain Type Classification,” in Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems, 2005. • 3 – H. Bischof, W. Schneider, and A.J. Pinz, “Multispectral Classification of Landsat-Images Using Neural Networks,” in IEEE Transactions on Geoscience and Remote Sensing, 1992. • 4 – N. Vandapel, D.F. Huber, A. Kapuria and M. Herbert, “Natural Terrain Classification using 3-D Ladar Data,” in Proceedings of the 2004 IEEE International Conference on Robotics and Automation, 2004. • 5 – K.S. Chen, S.K. Yen and D.W. Tsay, “Neural classification of SPOT imagery through integration of intensity and fractal information,” International Journal of Remote Sensing, 18:4, 763-783, 1997. • 6 – I. Davis, Neural Networks for Real-Time Terrain Typing, tech. report CMU-RI-TR-95-06, Robotics Institute, Carnegie Mellon University, January, 1995. • 7 – I.L. Davis and A. Stentz, “Sensor Fusion for Autonomous Outdoor Navigation Using Neural Networks,” tech. report CMU-RI-TR-95-05, Robotics Institute, Carnegie Mellon University, January 1995.