Data Association Using Visual Object Recognition for EKF-SLAM in Home Environment

Ahn, S. H.; Choi, Minyong; Choi, Jinwoo; Chung, Wan Kyun

doi:10.1109/iros.2006.281936

Cited by 32 publications

(21 citation statements)

References 10 publications

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“…The proposed method has an advantage over the previous works which only use either sonar features (Choi et al 2005) or visual objects (Ahn et al 2006), especially, in a large environment. Sonar-only SLAM works well in a small environment or local parts of a large environment.…”

Section: Introductionmentioning

confidence: 90%

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A practical approach for EKF-SLAM in an indoor environment: fusing ultrasonic sensors and stereo camera

et al. 2008

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Improving the practical capability of SLAM requires effective sensor fusion to cope with the large uncertainties from the sensors and environment. Fusing ultrasonic and vision sensors possesses advantages of both economical efficiency and complementary cooperation. In particular, it can resolve the false data association and divergence problem of an ultrasonic sensor-only algorithm and overcome both the low frequency of SLAM update caused by the computational burden and the weakness to illumination changes of a vision sensor-only algorithm. In this paper, we propose a VR-SLAM (Vision and Range sensor-SLAM) algorithm to combine ultrasonic sensors and stereo camera very effectively. It consists of two schemes: (1) extracting robust point and line features from sonar data and (2) recognizing planar visual objects using a multi-scale Harris corner detector and its SIFT descriptor from a pre-constructed object database. We show that fusing these schemes through EKF-SLAM frameworks can achieve correct data association via the object recognition and high frequency update via the sonar features. The performance of the proposed algorithm was verified by experiments in various real indoor environments.

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Section: Introductionmentioning

confidence: 90%

“…(1) extracting robust sonar features (Choi et al 2005), (2) recognizing visual objects (Ahn et al 2006) and (3) fusing both features via EKF (Extended Kalman Filter)-SLAM frameworks.…”

Section: Introductionmentioning

confidence: 99%

A practical approach for EKF-SLAM in an indoor environment: fusing ultrasonic sensors and stereo camera

et al. 2008

Self Cite

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“…Recall that if one of the vertices has been marked before, we need to reduce the content pertinence score with a small factor as a cost. The SIFT feature is invariant to image translation, rotation, scaling and partially invariant to illumination changes and affine, projective transformations (Ahn et al, 2006). It detects local peaks (key coordinates) which are local extremes of the Difference-of-Gaussian (DoG) images in various locations and scales.…”

Section: (Iii) Mapping Back To the Vertex Graphmentioning

confidence: 99%

Video content summarization and augmentation based on structural semantic processing and social network analysis

Chen

Wang

2010

Journal of the Chinese Institute of Engineers

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Video summarization techniques have been proposed for years to offer people comprehensive understanding of a whole story on video. However, although these traditional methods give brief summaries for users, they still do not provide conceptorganized or structural views. Besides, the knowledge they offer to users is often limited to existing videos. In this study, we present a structural video content summarization that utilizes the four kinds of entities, "who," "what," "where," and "when," to establish the framework of the video contents. Relevant media associated with each entity in the online resource are also analyzed to enrich existing contents.With the above-mentioned information, the structure of the story and its complementary knowledge can be built up according to the entities. Therefore, users can not only browse the video efficiently but also focus on what they are interested in. In order to construct the fundamental system, we employ the maximum entropy criterion to integrate visual and text features extracted from video frames and speech transcripts, generating high-level concept entities. Shots are linked together based on their contents. After constructing the relational graph, we exploit the graph entropy model to detect meaningful shots and relations. The social network analysis based on the Markov clustering algorithm is performed to explore relevant information online. The results demonstrate that our system can achieve excellent performance and information coverage.

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“…In SLAM, the target incrementally builds a consistent map of the environment while simultaneously determining its pose within this map. Filter-based SLAM [5,[7][8][9] processes the information from proprioceptive and exteroceptive sensors with filters, such as Extend Kalman Filter (EKF) and Particle Filter (PF), allowing an optimized estimated result to be obtained.…”

Section: Introductionmentioning

confidence: 99%

Kidnapping Detection and Recognition in Previous Unknown Environment

Tian

2017

Journal of Sensors

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An unaware event referred to as kidnapping makes the estimation result of localization incorrect. In a previous unknown environment, incorrect localization result causes incorrect mapping result in Simultaneous Localization and Mapping (SLAM) by kidnapping. In this situation, the explored area and unexplored area are divided to make the kidnapping recovery difficult. To provide sufficient information on kidnapping, a framework to judge whether kidnapping has occurred and to identify the type of kidnapping with filter-based SLAM is proposed. The framework is called double kidnapping detection and recognition (DKDR) by performing two checks before and after the "update" process with different metrics in real time. To explain one of the principles of DKDR, we describe a property of filter-based SLAM that corrects the mapping result of the environment using the current observations after the "update" process. Two classical filter-based SLAM algorithms, Extend Kalman Filter (EKF) SLAM and Particle Filter (PF) SLAM, are modified to show that DKDR can be simply and widely applied in existing filter-based SLAM algorithms. Furthermore, a technique to determine the adapted thresholds of metrics in real time without previous data is presented. Both simulated and experimental results demonstrate the validity and accuracy of the proposed method.

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Data Association Using Visual Object Recognition for EKF-SLAM in Home Environment

Cited by 32 publications

References 10 publications

A practical approach for EKF-SLAM in an indoor environment: fusing ultrasonic sensors and stereo camera

A practical approach for EKF-SLAM in an indoor environment: fusing ultrasonic sensors and stereo camera

Video content summarization and augmentation based on structural semantic processing and social network analysis

Kidnapping Detection and Recognition in Previous Unknown Environment

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