Large Scale Environment Partitioning in Mobile Robotics Recognition Tasks
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1 Large Scale Environment in Mobile Robotics Recognition Tasks Boyan Bonev, Miguel Cazorla Robot Vision Group Department of Computer Science and Artificial Intelligence University of Alicante April 27th, 27 Bonev, Cazorla Environment in Recognition Tasks 1 / 35 WAF 28
2 Outline 1 Localization and vision Initial approach approach Bonev, Cazorla Environment in Recognition Tasks 2 / 35 WAF 28
3 Outline Localization and vision Initial approach approach 1 Localization and vision Initial approach approach Bonev, Cazorla Environment in Recognition Tasks 3 / 35 WAF 28
4 Localization Localization and vision Initial approach approach Mobile robots Sensors + Maps Localization Bonev, Cazorla Environment in Recognition Tasks 4 / 35 WAF 28
5 Vision-based localization Localization and vision Initial approach approach Environments Ad-hoc Natural Indoor Outdoor Bonev, Cazorla Environment in Recognition Tasks 5 / 35 WAF 28
6 Localization and vision Initial approach approach Appearance based visual recognition Visual recognition approaches Structural-description Structure from high level features Appearance-based Images or low level features y69a1, y69a1, y69a1, y69a1, y69a1, y69a1, y69a1, y69a1, y69a1, y69a1, y69a2, y69a2, y69a2, y69a2, y69a2, y69a2, y69a2, y69a2, y69a2, y69a2, y69a3, y69a3, y69a3, y69a3, y69a3, y69a3, y69a3, y69a3, y69a3, y69a3, y69a4, y69a4, y69a4, y69a4, y69a4, y69a4, y69a4, y69a4, y69a4, y69a4, y69a5, y69a5, y69a5, y69a5, y69a5, y69a5, y69a5, y69a5, y69a5, y69a5,1 Bonev, Cazorla Environment in Recognition Tasks 6 / 35 WAF 28
7 Omnidirectional images Localization and vision Initial approach approach Omnidirectional images Local views with 36 o visibility Independence of the direction of the route. Convenient representation for rotation-invariant recognition Bonev, Cazorla Environment in Recognition Tasks 7 / 35 WAF 28
8 Omnidirectional images Localization and vision Initial approach approach Bonev, Cazorla Environment in Recognition Tasks 8 / 35 WAF 28
9 Feature selection approach Localization and vision Initial approach approach N F N S M M M 1-Fold CV Train Test Low level filters Nitzberg Canny, Gradient Color Filters Histograms comparison 2,4, and bins discretization Images # reference image Vectors Vectors All Features Selected F. Best F. Set Single similiarity function response H Error? # test image Bonev, Cazorla Environment in Recognition Tasks 9 / 35 WAF 28
10 Localization and vision Initial approach approach Large environments Bonev, Cazorla Environment in Recognition Tasks 1 / 35 WAF 28
11 Approach Localization and vision Initial approach approach Unsupervised partitioning of the environment multiple hypotheses MCL to select a single hypothesis gradient Gradient of the JR divergence # image # reference image Similiarity functions responses 4 H1 H2 H3 H4 3 H5 H6 H7 H8 H9 H1 2 H H H13 H14 H15 1 H16 H17 H18 H19 H # test image likelihood likelihood likelihood likelihood Particle filter, iterations 1,5,8, particle position (# reference image) Bonev, Cazorla Environment in Recognition Tasks / 35 WAF 28
12 Outline 1 Localization and vision Initial approach approach Bonev, Cazorla Environment in Recognition Tasks / 35 WAF 28
13 Sequence of images Bonev, Cazorla Environment in Recognition Tasks 13 / 35 WAF 28
14 Sequence of images Bonev, Cazorla Environment in Recognition Tasks 14 / 35 WAF 28
15 Sequence of images Bonev, Cazorla Environment in Recognition Tasks 15 / 35 WAF 28
16 Divide the problem How to divide the problem? Try all possible partitions Clustering algorithms Look for local variations in the information Bonev, Cazorla Environment in Recognition Tasks 16 / 35 WAF 28
17 Jensen-Rényi divergence Information-theoretic divergence measures entropy based unfeasible for multidimensional data Jensen-Rényi divergence α-entropy based feasible estimation in high-dimensional spaces (Hero and Michel, 22) J-R divergence applications may be defined between any number of probability distributions may be used to detect edges with a sliding window Region A Region B Data W1 W2 JR =.4 W1 W2 JR =.5 W1 W2 JR =.6 W1 W2 JR =.7 W1 W2 JR =.6 W1 W2 JR =.5 W1 W2 JR =.4 J-R divergence Bonev, Cazorla Environment in Recognition Tasks 17 / 35 WAF 28
18 Jensen-Rényi divergence J-R divergence simplified for two equally weighted distributions Data Region A Region B where JR α (p 1, p 2 ) = = H α ( p1 + p 2 2 ) H α(p 1 ) + H α (p 2 ), 2 Rényi entropy H α is estimated with Hero and Michel s method (based on minimal spanning trees) complexity depending on the number of samples O(N log N) W1 W2 JR =.4 W1 W2 JR =.5 W1 W2 JR =.6 W1 W2 JR =.7 W1 W2 JR =.6 W1 W2 JR =.5 W1 W2 JR =.4 J-R divergence Bonev, Cazorla Environment in Recognition Tasks 18 / 35 WAF 28
19 Jensen-Rényi divergence Response and entropy divergence analysis of Nitzberg filter Region A Region B Data W1 W1 W1 W2 W2 W2 JR =.4 JR =.5 JR =.6 W1 W1 W1 W2 W2 W2 JR =.7 JR =.6 JR =.5 response of Nitzberg filter local entropy divergence divergence gradient J-R divergence W1 W2 JR = # Image Bonev, Cazorla Environment in Recognition Tasks 19 / 35 WAF 28
20 Multiscale Jensen-Rényi divergence JR divergence at various window sizes #241 JR divergence # window size 1 # # image Bonev, Cazorla Environment in Recognition Tasks 2 / 35 WAF 28
21 Resulting partitions Gradient of the JR divergence gradient # image Bonev, Cazorla Environment in Recognition Tasks 21 / 35 WAF 28
22 Resulting partitions Bonev, Cazorla Environment in Recognition Tasks 22 / 35 WAF 28
23 Feature extraction Extraction of global features Rings Histograms 4 bins Filters Bank 1 2. K K = Img i C = 4 C x K Feature Vector N = C x K x (B-1) = 24 features F Bonev, Cazorla Environment in Recognition Tasks 23 / 35 WAF 28
24 Training Selection of features N F N S M M M 1-Fold CV Train Test Images Vectors Vectors Error All Features Selected F.? Best F. Set Bonev, Cazorla Environment in Recognition Tasks 24 / 35 WAF 28
25 NNs in the feature space The ideal case: F N F N F 4 F 3 F 2 F 1 Bonev, Cazorla Environment in Recognition Tasks 25 / 35 WAF 28
26 NNs in the feature space The wrong case: F N F N F 4 F 3 F 2 F 1 Bonev, Cazorla Environment in Recognition Tasks 26 / 35 WAF 28
27 Response on the test set Classifier trained for images Classifier trained for images # reference image 2 # reference image # test image # test image Bonev, Cazorla Environment in Recognition Tasks 27 / 35 WAF 28
28 Several localization hypotheses Similiarity functions responses 4 # reference image H1 H2 H3 H4 H5 H6 H7 H8 H9 H1 H H H13 H14 H15 H16 H17 H18 H19 H # test image Bonev, Cazorla Environment in Recognition Tasks 28 / 35 WAF 28
29 Similiarity functions responses # test image H1 H2 H3 H4 H5 H6 H7 H8 H9 H1 H H H13 H14 H15 H16 H17 H18 H19 H2 Several localization hypotheses # reference image Monte Carlo Localization, given: A motion model A likelihood function for a given position Bonev, Cazorla Environment in Recognition Tasks 29 / 35 WAF 28
30 Outline 1 Localization and vision Initial approach approach Bonev, Cazorla Environment in Recognition Tasks 3 / 35 WAF 28
31 MCL algorithm for disambiguation likelihood likelihood likelihood likelihood 1.5 Particle filter, iterations 1,5,8, particle position (# reference image) Bonev, Cazorla Environment in Recognition Tasks 31 / 35 WAF 28
32 Single classifier Single similiarity function response H 4 likelihood 1.5 Particle filter, iteration # reference image # reference image # test image Bonev, Cazorla Environment in Recognition Tasks 32 / 35 WAF 28
33 Outline 1 Localization and vision Initial approach approach Bonev, Cazorla Environment in Recognition Tasks 33 / 35 WAF 28
34 Visual localization approach Scalability Unsupervised IT-based partitioning Fast image recognition,.1sec Suitable for corridor-like scenarios Future work Generalize to 2D scenarios Bonev, Cazorla Environment in Recognition Tasks 34 / 35 WAF 28
35 Large Scale Environment in Mobile Robotics Recognition Tasks Boyan Bonev, Miguel Cazorla Robot Vision Group Department of Computer Science and Artificial Intelligence University of Alicante April 27th, 27 Bonev, Cazorla Environment in Recognition Tasks 35 / 35 WAF 28
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