Feature Based Landmark Extraction for Real Time Visual SLAM

Srinivasan, Natesh

doi:10.1109/artcom.2010.10

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…A landmark is defined as a set of distinguished features which are regularly observed and re-observed in the mapping environment. For a feature to be a landmark, it must satisfy a few criteria [68,72,75]:…”

Section: Landmarksmentioning

confidence: 99%

Image Retrieval Using Landmark Indexing for Indoor Navigation

Sinha

Ahmed

Greenspan

2014

2014 Canadian Conference on Computer and Robot Vision

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A novel approach is proposed for real-time retrieval of images from a large database of overlapping images of an indoor environment. The procedure extracts visual features from images using selected computer vision techniques, and processes the extracted features to create a reduced list of features annotated with the frame numbers they appear in. This method is named landmark indexing. Unlike some state-of-the-art approaches, the proposed method does not need to consider large image adjacency graphs because the overlap of the images in the map sufficiently increases information gain, and mapping of similar features to the same landmark reduces the search space to improve search efficiency. Empirical evidence from experiments on real datasets shows high (90-100%) accuracy in image retrieval, and improvement in search time from the order of 100-200 milliseconds to the order of 10-30 milliseconds. The image retrieval technique is also demonstrated by integrating it into a 3D real-time navigation system. This system is tested in several indoor environments and all experiments show accurate localization results in large indoor areas with errors in the order of 15-20 centimeters only. i Secondly, I am thankful to all the members of the Robotics and Computer Vision Laboratory (RCVLab). In particular, I would like to acknowledge Mirza Tahir

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Section: Landmarksmentioning

confidence: 99%

Image Retrieval Using Landmark Indexing for Indoor Navigation

Sinha

Ahmed

Greenspan

2014

2014 Canadian Conference on Computer and Robot Vision

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“…To allow intelligent robots to better integrate into everyday human life, indoor SLAM technology is also being developed. Vision sensors [1][2][3] and laser rangefinders (LRFs) [4,5] are also widely used for the SLAM work of the indoor mobile robots. Vision sensors are capable of providing visualization environmental maps that can be directly used for other tasks, while LRFs can provide precise range information.…”

Section: Introductionmentioning

confidence: 99%

Particle Smoother-Based Landmark Mapping for the SLAM Method of an Indoor Mobile Robot with a Non-Gaussian Detection Model

Wang

Takahashi

2019

Journal of Sensors

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HF-band radio-frequency identification (RFID) is a robust identification system that is rarely influenced by objects in the robot activity area or by illumination conditions. An HF-band RFID system is capable of facilitating a reasonably accurate and robust self-localization of indoor mobile robots. An RFID-based self-localization system for an indoor mobile robot requires prior knowledge of the map which contains the ID information and positions of the RFID tags used in the environment. Generally, the map of RFID tags is manually built. To reduce labor costs, the simultaneous localization and mapping (SLAM) technique is designed to localize the mobile robot and build a map of the RFID tags simultaneously. In this study, multiple HF-band RFID readers are installed on the bottom of an omnidirectional mobile robot and RFID tags are spread on the floor. Because the tag detection process of the HF-band RFID system does not follow a standard Gaussian distribution, extended Kalman filter- (EKF-) based landmark updates are unsuitable. This paper proposes a novel SLAM method for the indoor mobile robot with a non-Gaussian detection model, by using the particle smoother for the landmark mapping and particle filter for the self-localization of the mobile robot. The proposed SLAM method is evaluated through experiments with the HF-band RFID system which has the non-Gaussian detection model. Furthermore, the proposed SLAM method is also evaluated by a range and bearing sensor which has the standard Gaussian detection model. In particular, the proposed method is compared against two other SLAM methods: FastSLAM and SLAM methods utilize particle filter for both the landmark updating and robot self-localization. The experimental results show the validity and superiority of the proposed SLAM method.

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SLAM Method Based on Independent Particle Filters for Landmark Mapping and Localization for Mobile Robot Based on HF-band RFID System

Wang

Takahashi

2017

J Intell Robot Syst

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Feature Based Landmark Extraction for Real Time Visual SLAM

Cited by 4 publications

References 9 publications

Image Retrieval Using Landmark Indexing for Indoor Navigation

Image Retrieval Using Landmark Indexing for Indoor Navigation

Particle Smoother-Based Landmark Mapping for the SLAM Method of an Indoor Mobile Robot with a Non-Gaussian Detection Model

SLAM Method Based on Independent Particle Filters for Landmark Mapping and Localization for Mobile Robot Based on HF-band RFID System

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