Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient simplification of point-sampled surfaces
Proceedings of the conference on Visualization '02
Environment-specific novelty detection
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
A Metric for Distributions with Applications to Image Databases
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Novelty detection: a review—part 1: statistical approaches
Signal Processing
A Generic Methodology for Partitioning Unorganised 3D Point Clouds for Robotic Vision
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Visual novelty detection with automatic scale selection
Robotics and Autonomous Systems
International Journal of Computer Vision
Towards 3D Point cloud based object maps for household environments
Robotics and Autonomous Systems
ACM Computing Surveys (CSUR)
DP-SLAM: fast, robust simultaneous localization and mapping without predetermined landmarks
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
LIDAR and vision-based pedestrian detection system
Journal of Field Robotics
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Anytime online novelty and change detection for mobile robots
Journal of Field Robotics
Robotics and Autonomous Systems
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This article proposes a framework to detect and segment changes in robotics datasets, using 3D robotic mapping as a case study. The problem is very relevant in several application domains, not necessarily related with mobile robotics, including security, health, industry and military applications. The aim is to identify significant changes by comparing current data with previous data provided by sensors. This feature is extremely challenging because large amounts of noisy data must be processed in a feasible way. The proposed framework deals with novelty detection and segmentation in robotic maps using clusters provided by Gaussian Mixture Models (GMMs). GMMs provides a feature space that enables data compression and effective processing. Two alternative criteria to detect changes in the GMM space are compared: a greedy technique based on the Earth Mover's Distance (EMD); and a structural matching algorithm that fulfills both absolute (global matching) and relative constraints (structural matching). The proposed framework is evaluated with real robotic datasets and compared with other methods known from literature. With this purpose, 3D mapping experiments are carried out with both simulated data and real data from a mobile robot equipped with a 3D range sensor.