Mobile Robot Navigation with Intelligent Infrared Image Interpretation

Mobile Robot Navigation with Intelligent Infrared Image Interpretation

William L. Fehlman

Language: English

Pages: 274

ISBN: 1447156943

Format: PDF / Kindle (mobi) / ePub

Mobile Robot Navigation with Intelligent Infrared Image Interpretation

William L. Fehlman

Language: English

Pages: 274

ISBN: 1447156943

Format: PDF / Kindle (mobi) / ePub


Mobile robots require the ability to make decisions such as "go through the hedges" or "go around the brick wall." Mobile Robot Navigation with Intelligent Infrared Image Interpretation describes in detail an alternative to GPS navigation: a physics-based adaptive Bayesian pattern classification model that uses a passive thermal infrared imaging system to automatically characterize non-heat generating objects in unstructured outdoor environments for mobile robots. The resulting classification model complements an autonomous robot’s situational awareness by providing the ability to classify smaller structures commonly found in the immediate operational environment.

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active sensor system, a source simply transmits some pulse of energy from the robot’s platform and onboard sensors receive the energy after being reflected from an object in the path. The bot’s intelligence software analyzes contrasting information in the reflected signals received within the field of view of the sensor to determine the ranges, sizes, and locations of objects. Consequently, detection usually coincides with obstacle avoidance. Thus, the bot simply knows the location and size of an

thermal scene. (a) Posterior probabilities for the wood wall feature vectors and (b) macro feature values with variations in window size indexed from 1 (largest window) to 100 (smallest window). 154 Visible image and thermal images for the pine tree log used in the sensitivity analysis for the variations in the rotational orientation. (a) 0°, (b) 45°, (c) 90°, (d) 135°, (e) 180°. The portion of the pine tree log segmented for the analysis is enclosed by the solid rectangular borders in each

vectors associated with various classifiers for both the extend and compact object classes. Moreover, the most favorable feature vectors are those that contain contributions from all the feature types – meteorological, micro, and macro. 4.1 Introduction In the previous chapter, we generated 21 thermal features from three categories – meteorological, micro, and macro. This chapter will present the third step in our pattern classification model design process – thermal feature selection. In the

portion is used for testing. Thus, the training and test data sets are disjoint. In this case, the training set is the data collected from 15 March to 22 June 2007. We will use the test set collected from 25 June to 3 July 2007 in Sects. 4.5.5 and 4.5.6 to assess the performance of the Bayesian, KNN, and Parzen classifiers. We will use our blind data set that was collected from 6 July to 5 November as our validation set when we analyze our most favorable feature vectors and designing our novel

Normal Distance (Pattern to Eigenvector) Brick Wall 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0 0 0.2 0.4 0.6 0 0.8 0 0.2 0.4 0.6 0.8 Wood Wall Picket Fence 0.8 1.5 0.6 1 0.4 0.5 0.2 0 0 0.5 1 0 1.5 0 0.5 1 1.5 Component (Pattern onto Eigenvector) Pattern = <2 3 5 6 9 13 18> Hedges Brick Wall 1 0.8 Normal Distance (Pattern to Eigenvector) 0.6 0.5 0.4 0.2 0 0 0.2 0.4 0.6 0 0.8 0 0.2 0.4 0.6 0.8 Wood Wall Picket Fence 0.8 1 0.6 0.4 0.5 0.2 0 0 0.2

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