Indoor localization using line based map for autonomous mobile robot

Proceedings of the 2011 ACM Symposium on Applied Computing

2011

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.

Section: Related Workmentioning

confidence: 99%

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

Proceedings of the 2011 ACM Symposium on Applied Computing

2011

“…This method, which considers scans as collections of line segments, works without any knowledge about the robot pose. In [13] an indoor localization method based on segment-based maps is proposed. It works in four steps: clustering scan data; feature extraction from laser data; line-based matching; and pose prediction.…”

Section: Related Workmentioning

confidence: 99%

Feature-Based Mapping Using Incremental Gaussian Mixture Models

2010 Latin American Robotics Symposium and Intelligent Robotics Meeting

2010

This paper proposes a new algorithm for featurebased 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 with the proposed model prototype show that it is able to build accurate environment representations using real data provided by a mobile robot.

“…This method, which considers scans as collections of line segments, works without any knowledge about the robot pose. In Luo et al (2008) an indoor localization method based on segment-based maps is proposed. It works in four steps: clustering scan data; feature extraction from laser data; linebased matching; and pose prediction.…”

Section: Related Workmentioning

confidence: 99%

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

2011

jacr

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.