Feature-Based Mapping Using Incremental Gaussian Mixture Models

Heinen, Milton Roberto; Engel, Paulo Martins

doi:10.1109/lars.2010.13

Cited by 5 publications

(2 citation statements)

References 22 publications

(18 reference statements)

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“…Although the proposed model can be used either with sonar or laser range data, in this paper we have used just data provided by sonar sensors because the mapping process is more difficult with this kind of sensor than laser scanners. The use of laser range data in the proposed model is described in a previous paper [11]. Figure 2 shows the Pioneer 3-DX robot, the robotic platform used in these experiments.…”

Section: Resultsmentioning

confidence: 99%

Incremental feature-based mapping from sonar data using Gaussian mixture models

Heinen

Engel

2011

Proceedings of the 2011 ACM Symposium on Applied Computing

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This paper proposes a new algorithm for feature-based environment mapping where the environment is represented using multivariate Gaussian mixture models. This algorithm, which can be used either with sonar or laser range data, is able to create and maintain environment maps in real time using few memory requirements. Moreover, it does not assume that the environment is composed by linear structures and allows computing the occupancy probabilities of any map position very fast and without introducing discretization errors. The experiments performed using sonar data show that it is able to build accurate environment representations using noisy data provided by a mobile robot.

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Section: Resultsmentioning

confidence: 99%

Incremental feature-based mapping from sonar data using Gaussian mixture models

Heinen

Engel

2011

Proceedings of the 2011 ACM Symposium on Applied Computing

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“…This section presents a new feature-based mapping algorithm, proposed in (Heinen and Engel, 2011;Heinen and Engel, 2010e), which uses the IGMN probabilistic units to represent the features perceived in the environment. This kind of representation, which is inherently probabilistic, is more effective than segmentbased maps because it has an arbitrary accuracy (it does not require discretization) and can even model objects that do not provide line segments.…”

Section: Fe Ature-based Mappingmentioning

confidence: 99%

IGMN: An incremental connectionist approach for concept formation, reinforcement learning and robotics

Heinen

Engel

2011

jacr

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This paper demonstrates the use of a new connectionist approach, called IGMN (standing for Incremental Gaussian Mixture Network) in some state-of-the-art research problems such as incremental concept formation, reinforcement learning and robotic mapping. IGMN is inspired on recent theories about the brain, especially the Memory-Prediction Framework and the Constructivist Artificial Intelligence, which endows it with some special features that are not present in most neural network models such as MLP, RBF and GRNN. Moreover, IGMN is based on strong statistical principles (Gaussian mixture models) and asymptotically converges to the optimal regression surface as more training data arrive. Through several experiments using the proposed model it is also demonstrated that IGMN learns incrementally from data flows (each data can be immediately used and discarded), it is not sensible to initialization conditions, does not require fine-tuning its configuration parameters and has a good computational performance, thus allowing its use in real time control applications. Therefore, IGMN is a very useful machine learning tool for concept formation and robotic tasks.

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A GPU-accelerated Approximate Algorithm for Incremental Learning of Gaussian Mixture Model

Chen

Zhang

et al. 2012

2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops &Amp; PhD Forum

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The Gaussian mixture model (GMM) is a widely used probabilistic clustering model. The incremental learning algorithm of GMM is the basis of a variety of complex incremental learning algorithms. It is typically applied to realtime or massive data problems where the standard Expectation Maximum (EM) algorithm does not work. But the output of the incremental learning algorithm may exhibit degraded cluster quality than the standard EM algorithm. In order to achieve a high-quality and fast incremental GMM learning algorithm, we develop an algorithmic method for incremental learning of GMM in a GPU-CPU hybrid system. Our method uses model evolution history to approximate the model order and adopts both hypothesis-test and Euclidean distance to do mixture component equality test. Through experiments we show that our method achieves high performance in terms of both cluster quality and speed.

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Feature-Based Mapping Using Incremental Gaussian Mixture Models

Cited by 5 publications

References 22 publications

Incremental feature-based mapping from sonar data using Gaussian mixture models

Incremental feature-based mapping from sonar data using Gaussian mixture models

IGMN: An incremental connectionist approach for concept formation, reinforcement learning and robotics

A GPU-accelerated Approximate Algorithm for Incremental Learning of Gaussian Mixture Model

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