Outdoor localization in mobile robot with 3d lidar based on principal component analysis and k-nearest neighbors Algorithm

Abstract EN

Localization is one of the potential challenges for a mobile robot.

Due to the inaccuracy of GPS systems in determining the location of the moving robot alongside weathering effects on sensors such as RGBs (e.

g.

rain and light-sensitivity(.

This paper aims to improve the localization of mobile robots by combining the 3D LiDAR data with RGB-D images using deep learning algorithms.

The proposed approach is to design an outdoor localization system.

It is divided into three stages.

The first stage is the training stage where 3D LiDAR scans the city and then reduces the dimensions of 3D LiDAR data to 2.5D image.

This is based on PCA method where these data are used as training data.

The second stage is the testing data stage.

RGB and depth image in IHS method are combined to generate 2.5D fusion image.

The training and testing of these datasets are based on using Convolution Neural Network.

The third stage consists of using the K-Nearest Neighbor algorithm.

This is the classification stage to get high accuracy and reduces the training time.

The experimental results obtained prove the superiorly of the proposed approach with accuracy up to 97.52% , Mean Square of Error of 0.057568, and Mean error in distance equals 0.804 Localization is one of the potential challenges for a mobile robot.

Due to the inaccuracy of GPS systems in determining the location of the moving robot alongside weathering effects on sensors such as RGBs (e.

g.

rain and light-sensitivity(.

This paper aims to improve the localization of mobile robots by combining the 3D LiDAR data with RGB-D images using deep learning algorithms.

The proposed approach is to design an outdoor localization system.

It is divided into three stages.

The first stage is the training stage where 3D LiDAR scans the city and then reduces the dimensions of 3D LiDAR data to 2.5D image.

This is based on PCA method where these data are used as training data.

The second stage is the testing data stage.

RGB and depth image in IHS method are combined to generate 2.5D fusion image.

The training and testing of these datasets are based on using Convolution Neural Network.

The third stage consists of using the K-Nearest Neighbor algorithm.

This is the classification stage to get high accuracy and reduces the training time.

The experimental results obtained prove the superiorly of the proposed approach with accuracy up to 97.52% , Mean Square of Error of 0.057568, and Mean error in distance equals 0.804 meters.