This paper describes an approach for using several levels of data fusion in the domain of autonomous off-road navigation. We are focusing on outdoor obstacle detection, and we present techniques that leverage on data fusion and machine learning for increasing the reliability of obstacle detection systems. We are combining color and infrared (IR) imagery with range information from a laser range finder. We show that in addition to fusing data at the pixel level, performing high level classifier fusion is beneficial in our domain. Our general approach is to use machine learning techniques for automatically deriving effective models of the classes of interest (obstacle and nonobstacle for example). We train classifiers on different subsets of the features we extract from our sensor suite and show how different classifier fusion schemes can be applied for obtaining a multiple classifier system that is more robust than any of the classifiers presented as input. We present experimental results we obtained on data collected with both the eXperimental Unmanned Vehicle (XUV) and a CMU developed robotic tractor.
No abstract
Abstract-Real-world applications of mobile robotics call for increased autonomy, requiring reliable perception systems. Since manually tuned perception algorithms are difficult to adapt to new operating environments, systems based on supervised learning are necessary for future progress in autonomous navigation.Data labeling is a major concern when supervised learning is applied to the large-scale problems occuring in realistic robotics applications. We believe that algorithms for automatically selecting important data for labeling are necessary, and propose to employ active learning techniques to reduce the amount of labeling required to learn from a data set.In this paper we show that several standard active learning algorithms can be adapted to meet specific constraints characteristic to our domain, such as the need to learn from data with severely unbalanced class priors. We validate the solutions we propose by extensive experimentation on multiple realistic data sets captured with a robotic vehicle. Based on our results for the task of obstacle detection, we conclude that active learning techniques are applicable to our domain, and they can lead to significant reductions in the labeling effort required to use supervised learning in outdoor perception.
Absrmct-Autonomous navigation in outdoor, om-road environments requires solving complex classification problems. Obstacle detection, road following and terrain classification are examples of tasks which have been successfully approached using supervised machine learning techniques for classification. Large amounts of training data are usually necessary in order to achieve satisfactory generalization. In such .cases, manually labeling data becomes an expensive and tedious process.This paper describes a method for reducing the amount of data that needs to he presented to a human trainer. The algorithm relies on kernel density estimation in order to identify "interesting" scenes in a dataset. Our method does not q u i r e any interaction with a human expert for selecting the images; and only minimal amounts of tuning are necessary.We dimonstrate ils effectiveness in several experiments using data collected with two diNerent vehicles. We first show that our method automatically selects those scenes from a large dataset that a person would consider "important" for classification tasks. Secondly, we show that hy labeling only few of the images selected hy our method, we obtain classification performance that is comparable to the one reached after labeling hundreds of images from the same dataset. 0-7803-8232-3/04/$17.00 WOO4 IEEE I _ on Research and dwelopmenr in informarion retrieval. Springer-Verlag New York. Inc., t994, pp. 3-12, H. Seung, M. Opper. and H. Sampolinsky, "Query by comminee:' in Pmceedings ofthe Fifth IVorkrhop on Compuroional Leoming Theocv, San Mateo, CA, 1992, pp. 287-294. C. M. Bishop, Neuml Ncworkr for pan ern^ Recoxnirion. Oxford University kiss: 1997. T. Hastie. R. libshirani. and J. Friedman. 7'he Elemenrr 2 Smrirrical ~enming,' ser. springer Series in Statistics. A. Gray and A. Moon, "Rapid evaluation of multiple density models," in Anificiol Intelligence m d Sforisrim, 2003. -. "'N-Body' pmbleb in staristical learning:' in Advances in Neural Infomurrion Pmcesring Sprema, T. K. Leen and T. 0. Dienerich, EA% M m Press. 2001. A. Moore, "Very fast EM-based mixture model clusrering using multiresolution KI-ma," in Adwnccs in Neuml Informarion Pmcessing
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