Outdoor Localization in Mobile Robot with 3D LiDAR Based on Principal Component Analysis and K-Nearest Neighbors Algorithm

Atiyah, Hanan A.; Mohammed, Younis

doi:10.30684/etj.v39i6.2032

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…It is made up of a set of mathematical operations such as convolution. Image pixels are stored in a two-dimensional matrix [33]. 3.…”

Section: Deep Learning Algorithmsmentioning

confidence: 99%

Outdoor Localization for a Mobile Robot under Different Weather Conditions Using a Deep Learning Algorithm

Atiyah¹,

Hassan²

2023

JESA

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A fundamental issue in robotics is the precise localization of mobile robots in uncertain environments. Due to changing environmental patterns and lighting, localization under difficult perceptual conditions remains problematic. This paper presents an approach for locating an outdoor mobile robot using deep learning algorithms merge with 3D Light Detection and Ranging LiDAR data and RGB-D image. This approach is divided into three levels. The first is the training level, which involves scanning the localization area with a 3D LiDAR sensor and then converting the data into a 2.5D image based on the Principal Component Analysis. The testing is the second level in the Intensity Hue Saturation process. Then, the RGB and Depth images are combined to create a 2.5D fusion image. These datasets are trained and tested using Convolution Neural Networks. The K-Nearest Neighbor algorithm is used in the third level is the classification. The results show that the proposed approach is better in terms of accuracy of 97.46% and the Mean error distance is 0.6 meters.

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“…It is made up of a set of mathematical operations such as convolution. Image pixels are stored in a two-dimensional matrix [33]. 3.…”

Section: Deep Learning Algorithmsmentioning

confidence: 99%

Outdoor Localization for a Mobile Robot under Different Weather Conditions Using a Deep Learning Algorithm

Atiyah¹,

Hassan²

2023

JESA

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“…Formulas in geometry can be used to convert RGB to IHS. The H that is given by [47] (Equation 4 and 5).…”

Section: ( ) √mentioning

confidence: 99%

Localization for self-driving vehicles based on deep learning networks and RGB cameras

2023

IJATEE

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Localization is a crucial aspect of the operation and safety of autonomous vehicles (AV). It pertains to the AV's ability to precisely determine and uphold its position and orientation within its environment. Accurate localization is vital for the vehicle's navigation, obstacle avoidance, route planning, and overall dependable functionality. The navigation process for an AV encompasses establishing its starting point, identifying the destination, and determining the route to move from one point to another (known as the path planning issue). *Author for correspondenceThe AV's success in this process heavily relies on its localization capabilities, ensuring it can accurately locate itself in its surroundings [1].AVs utilize a blend of sensor inputs and advanced algorithms to determine their position relative to the surrounding environment, as shown in Figure 1. These sensors may consist of the global positioning system (GPS), the inertial measurement unit (IMU), light detection and ranging (LiDAR), radio detection and ranging (RADAR), and cameras. Each sensor offers unique data types, and their seamless integration is crucial for achieving precise localization. By combining the information from these diverse sensors, AVs can accurately navigate and operate in complex and dynamic environments

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“…The transform kernel is a 3 x 3 matrix. According to various published studies, the following definition employs distinct IHS transformations, resulting in substantial differences in matrix values [22]:…”

Section: Intensity Hue Saturation (Ihs) For Rgb and Depth Mergingmentioning

confidence: 99%

CNN-based Visual Localization for Autonomous Vehicles under Different Weather Conditions

Ghintab¹,

Hassan²

2022

ETJ

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 A Convolutional Neural Network (CNN) was developed for autonomous localization layerby-layer in urban driving situations.  To check the positional accuracy of the CNN, RGB images are combined with depth images using the IHS method.  With an accuracy rate of 94.74%, the simulation results demonstrated the effectiveness of the suggested strategy.  The Simulation findings demonstrate the superiority of the suggested technique for different weather conditions. Autonomous vehicles (AV) are expected to improve, transform, and revolutionize ground transportation. Previous techniques are dependent on localization employing pricey inaccuracy Global Positioning sensor. Furthermore, the performance loss is caused by drifting errors of Simultaneous Localization and M apping. Regarding categorizing and analyzing texture, cameras are much more accessible and practical. This work contributes to obtaining high accuracy for AV localization and reducing errors in predicted positions. Based on the light, accurate, and robust proposed Convolutional Neural Network (CNN), it will scale down the computational complexity and shorten the training time. Considering various weather and time of day conditions such as bright sunny, hard rain noon, and wet cloudy noon with a vision-only system Red, Green, and Blue (RGB) lowcost camera sensors. To check the positional accuracy of the CNN, RGB images are combined with depth images using the IHS method. The k-M ean technique evaluates the similarity between a specific image and all street images to obtain precise coordinates. The Simulation findings demonstrate the superiority of the suggested technique for different weather conditions, which has an accuracy of up to 94.74% and a M ean Squared Error M SE in a distance of 0 meters, as opposed to [19], where the M SE in the projected position is 4.8 meters. Another indication of the proposed method's effectiveness is that it yielded reliable results when its validity was tested on images from a dataset that had not been trained.

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Outdoor Localization in Mobile Robot with 3D LiDAR Based on Principal Component Analysis and K-Nearest Neighbors Algorithm

Cited by 6 publications

References 23 publications

Outdoor Localization for a Mobile Robot under Different Weather Conditions Using a Deep Learning Algorithm

Outdoor Localization for a Mobile Robot under Different Weather Conditions Using a Deep Learning Algorithm

Localization for self-driving vehicles based on deep learning networks and RGB cameras

CNN-based Visual Localization for Autonomous Vehicles under Different Weather Conditions

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