A laser-vision based obstacle detection and distance estimation for smart wheelchair navigation

Utaminingrum, Fitri; Kurniawan, Tri Astoto; Fauzi, Muhammad Ali; Maulana, Rizal; Syauqy, Dahnial; Wihandika, Randy Cahya; Sari, Yuita Arum; Adikara, Putra Pandu

doi:10.1109/siprocess.2016.7888236

Cited by 25 publications

(5 citation statements)

References 6 publications

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“…Aside from the previous method discussed above, some research presents unique approach in the development of smart wheelchair. Utaminingrum et al proposed an accurate obstacle distance estimation and navigation technique using camera and laser sensor [36]. Images gathered from the environment used to obtained the information on pathway condition, combined with laser sensor for obstacle recognition.…”

Section: E Other Control Methodsmentioning

confidence: 99%

Smart Wheelchairs: A Review on Control Methods

Aziz

Khusaini

Mohamed

et al. 2022

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)

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Recent advancement in research fields involving computer science and Artificial Intelligence has influence the rapid growth in smart wheelchair research. Another factor adding to this growth is the evolution of sensor and computing technology. Basically, a smart wheelchair is a powered wheelchair that has been modified by adding necessary sensors and instruments that able to read, collect and send information that can be used to modify the status of the wheelchair, as well as interacting with the environment or the user. The modification can be done on various part of the wheelchair system, and can be categorized with their input methods, operating system or their navigation system itself. This paper discussing on various input methods used in recent smart wheelchair research and summarizes the ideas presented.

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Section: E Other Control Methodsmentioning

confidence: 99%

Smart Wheelchairs: A Review on Control Methods

Aziz

Khusaini

Mohamed

et al. 2022

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)

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“…The solution using the infrastructure concept from Ref. [23], which represents an imaging mechanism with a blob-guided reference point, combines the pattern recognition of line laser image shapes at a certain angle. The estimation is based on the blobs–gaps relation of the position of the laser projector and the camera coordinates.…”

Section: State Of the Artmentioning

confidence: 99%

Photography Trilateration Indoor Localization with Image Sensor Communication

Jang

2019

Sensors

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Localization has become an important aspect in a wide range of mobile services with the integration of the Internet of things and service on demand. Numerous mechanisms have been proposed for localization, most of which are based on the estimation of distances. Depending on the channel modeling, each mechanism has its advantages and limitations on deployment, exhibiting different performances in terms of error rates and implementation. With the development of technology, these limitations are rapidly overcome with hybrid systems and enhancement schemes. The successful approach depends on the achievement of a low error rate and its controllability by the integration of deployed products. In this study, we propose and analyze a new distance estimation technique employing photography and image sensor communications, also named optical camera communications (OCC). It represents one of the most important steps in the implemented trilateration localization scheme with real architectures and conditions of deployment which is the second our contribution for this article. With the advantages of the image sensor hardware integration in smart mobile devices, this technology has great potential in localization-based optical wireless communication

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“…However, these different road models have the potential to cause problem, therefore, a better and more accurate method is needed to classify road surface conditions [10]. Some researchers use the GLCM classification method for describing texture details into spatial domain and edge images [9], [11]. Moreover, GLCM is also used to detect rock images in the road type classification based on visual data, the GLCM method is also used to characterize way surfaces by several aspects such as texture [12], color [13], and border features of riders' sight image to coach a neural network of objects.…”

Section: Introductionmentioning

confidence: 99%

Machine Vision for the Various Road Surface Type Classification Based on Texture Feature

Marianingsih¹,

Widodo²,

Pieter³

et al. 2022

JMEST

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The mechanized ability to specify the way surface type is a piece of key enlightenment for autonomous transportation machine navigation like wheelchairs and smart cars. In the present work, the extracted features from the object are getting based on structure and surface evidence using Gray Level Co-occurrence Matrix (GLCM). Furthermore, K-Nearest Neighbor (K-NN) Classifier was built to classify the road surface image into three classes, asphalt, gravel, and pavement. A comparison of KNN and Naïve Bayes (NB) was used in present study. We have constructed a road image dataset of 450 samples from real-world road images in the asphalt, gravel, and pavement. Experiment result that the classification accuracy using the K-NN classifier is 78%, which is better as compared to Naïve Bayes classifier which has a classification accuracy of 72%. The paving class has the smallest accuracy in both classifier methods. The two classifiers have nearly the same computing time, 3.459 seconds for the KNN Classifier and 3.464 seconds for the Naive Bayes Classifier.

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A laser-vision based obstacle detection and distance estimation for smart wheelchair navigation

Cited by 25 publications

References 6 publications

Smart Wheelchairs: A Review on Control Methods

Smart Wheelchairs: A Review on Control Methods

Photography Trilateration Indoor Localization with Image Sensor Communication

Machine Vision for the Various Road Surface Type Classification Based on Texture Feature

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