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

Heinen, Milton Roberto; Engel, Paulo Martins

doi:10.1145/1982185.1982484

Cited by 4 publications

(3 citation statements)

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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%

“…If the global model is still empty, all local units are added to it and deleted from the local model. Otherwise the robot pose is adjusted to minimize the differences between the local and global models using a component matching process described in Heinen and Engel (2011). Both IGMN models are then merged into the global model and the local model is emptied.…”

Section: Mapping Using Igmnmentioning

confidence: 99%

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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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Section: Fe Ature-based Mappingmentioning

confidence: 99%

Section: Mapping Using Igmnmentioning

confidence: 99%

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

Heinen

Engel

2011

jacr

Self Cite

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“…Recently, hybrid integration of feature, topological, and grid mapping has been developed [ 11 , 12 ]. Heinen [ 13 ] introduced a Gaussian mixture model to represent the surrounding environment and proposed a new feature-based environment mapping algorithm with the advantages of small memory usage and high processing speed, avoiding discrete errors in fast calculation. Ismail et al [ 14 ] used the data measured by a double ultrasonic sensor and proposed a fusion algorithm for environment feature extraction in which circular-arc feature extraction was combined with Hough transform-based TBF.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction and Mapping Construction for Mobile Robot via Ultrasonic MDP and Fuzzy Model

Long

et al. 2018

Sensors

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This paper presents a modeling approach to feature classification and environment mapping for indoor mobile robotics via a rotary ultrasonic array and fuzzy modeling. To compensate for the distance error detected by the ultrasonic sensor, a novel feature extraction approach termed “minimum distance of point” (MDP) is proposed to determine the accurate distance and location of target objects. A fuzzy model is established to recognize and classify the features of objects such as flat surfaces, corner, and cylinder. An environmental map is constructed for automated robot navigation based on this fuzzy classification, combined with a cluster algorithm and least-squares fitting. Firstly, the platform of the rotary ultrasonic array is established by using four low-cost ultrasonic sensors and a motor. Fundamental measurements, such as the distance of objects at different rotary angles and with different object materials, are carried out. Secondly, the MDP feature extraction algorithm is proposed to extract precise object locations. Compared with the conventional range of constant distance (RCD) method, the MDP method can compensate for errors in feature location and feature matching. With the data clustering algorithm, a range of ultrasonic distances is attained and used as the input dataset. The fuzzy classification model—including rules regarding data fuzzification, reasoning, and defuzzification—is established to effectively recognize and classify the object feature types. Finally, accurate environment mapping of a service robot, based on MDP and fuzzy modeling of the measurements from the ultrasonic array, is demonstrated. Experimentally, our present approach can realize environment mapping for mobile robotics with the advantages of acceptable accuracy and low cost.

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