Outdoor Localization of 4-Wheels for Mobile Robot Using CNN with 3D Data

Hanan A. Atiyah, Mohammed Y. Hassan


One of the possible problems for a mobile robot is the localization. This is due to GPS systems' difficulty in detecting the location of a moving robot and the effects of weathering on sensors, such as the light sensitivity of RGBs sensors. In addition, mapping techniques in severe environments requires time and effort. This research seeks to enhance the localization of mobile robots by merging 3D LiDAR data with RGB-D images and using deep learning techniques. The suggested method entails using a simulator to design a four-wheel mobile robot controlled by a LiDAR sensor and testing them in an outdoor environment. The proposed localization system works in three steps. The first step is the training step, in which the 3D point cloud LiDAR sensor scans the entire city and then uses the PCA method to compress the dimensions of the 3D LiDAR data to a 2.5D image. The testing data stage is the second step. First, the RGB and depth images have merged using the IHS technique to create a 2.5D fusion image. Next, Convolution Neural Networks are used to train and test these datasets to extract features from the images. Finally, the K-Nearest Neighbor method was used in the third step. The classification step allows high accuracy while also reducing training time. The experimental findings show that the suggested technique is better in yielding results up to an accuracy of 98.15 % and a Mean Square Error of 0.25, and the Mean Error Distance is 1.36 meters.


CNN; HIS; K-NN; LiDAR; outdoor localization; pca; mobile robot.

Full Text:



R. T. Kamil, M. J. Mohamed, and B. K. Oleiwi, "Path Planning of Mobile Robot Using Improved Artificial Bee Colony Algorithm," Eng. Technol. J., vol. 38, no. 9A, pp. 1384–1395, 2020, doi: 10.30684/etj.v38i9a.1100.

X. Bai, Z. Zhang, L. Liu, X. Zhai, J. Panneerselvam, and L. Ge, "Enhancing localization of mobile robots in distributed sensor environments for reliable proximity service applications," IEEE Access, vol. 7, pp. 28826–28834, 2019, doi: 10.1109/ACCESS.2019.2899059.

M. Y. Hassan and M. Z. A. Karam, "Design and Implementation of Rehabilitation Robot for Human Arm Movements," IRAQI J. Comput. Commun. Control Syst. Eng., vol. 15, no. 2, 2015.

N. A. K. Zghair and A. S. Al-Araji, "A one decade survey of autonomous mobile robot systems," Int. J. Electr. Comput. Eng., vol. 11, no. 6, pp. 4891–4906, 2021.

T. D. Dung, D. Hossain, S. ichiro Kaneko, and G. Capi, "Multifeature image indexing for robot localization in textureless environments," Robotics, vol. 8, no. 2, pp. 1–11, 2019, doi: 10.3390/ROBOTICS8020037.

N. C. Mithun, K. Sikka, H.-P. Chiu, S. Samarasekera, and R. Kumar, "Rgb2lidar: Towards solving large-scale cross-modal visual localization," in Proceedings of the 28th ACM International Conference on Multimedia, 2020, pp. 934–954.

X. Kang, J. Li, X. Fan, and W. Wan, "Real-Time RGB-D Simultaneous Localization and Mapping Guided by Terrestrial LiDAR Point Cloud for Indoor 3-D Reconstruction and Camera Pose Estimation," Appl. Sci., vol. 9, no. 16, p. 3264, 2019.

X. Li, S. Du, G. Li, and H. Li, "Integrate point-cloud segmentation with 3d lidar scan-matching for mobile robot localization and mapping," Sensors (Switzerland), vol. 20, no. 1, 2020, doi: 10.3390/s20010237.

K. Atsuzawa, S. Nilwong, D. Hossain, S. Kaneko, and G. Capi, "Robot navigation in outdoor environments using odometry and convolutional neural network," In: IEEJ international workshop on sensing, actuation, motion control, and optimization (SAMCON) 2019.

H. Sinha, J. Patrikar, E. G. Dhekane, G. Pandey, and M. Kothari, "Convolutional Neural Network Based Sensors for Mobile Robot Relocalization," in 2018 23rd International Conference on Methods & Models in Automation & Robotics (MMAR), 2018, pp. 774–779.

C. Debeunne and D. Vivet, "A review of visual-LiDAR fusion based simultaneous localization and mapping," Sensors, vol. 20, no. 7, p. 2068, 2020.

S. Oishi, Y. Inoue, J. Miura, and S. Tanaka, "SeqSLAM++: View-based robot localization and navigation," Rob. Auton. Syst., vol. 112, pp. 13–21, 2019.

Y. Duan, C. Yang, H. Chen, W. Yan, and H. Li, "Low-complexity point cloud denoising for LiDAR by PCA-based dimension reduction," Opt. Commun., vol. 482, no. 2019, 2021, doi: 10.1016/j.optcom.2020.126567.

Y. Duan, C. Yang, H. Chen, W. Yan, and H. Li, "Low-complexity point cloud denoising for LiDAR by PCA-based dimension reduction," Opt. Commun., vol. 482, p. 126567, 2021.

C.-B. Hsu, J.-C. Lee, and T.-M. Tu, "Generalized IHS-BT framework for the pansharpening of high-resolution satellite imagery," J. Appl. Remote Sens., vol. 12, no. 04, p. 1, 2018, doi: 10.1117/1.jrs.12.046008.

S. Agustin, H. Tjandrasa, and R. V. H. Ginardi, "Deep Learning-based Method for Multi-Class Classification of Oil Palm Planted Area on Plant Ages Using Ikonos Panchromatic Imagery," Int. J. Adv. Sci. Eng. Inf. Technol., vol. 10, no. 6, p. 2200, 2020, doi: 10.18517/ijaseit.10.6.12030.

A. A. Abdulhussein and F. A. Raheem, "Hand Gesture Recognition of Static Letters American Sign Language (ASL) Using Deep Learning," Eng. Technol. J., vol. 38, no. 6A, pp. 926–937, 2020, doi: 10.30684/etj.v38i6a.533.

L. Alzubaidi, J. Zhang,A. Humaidi,A. Al-Dujaili,Y. Duan, O. Al-Shamma, ... & L. Farhan, "Review of deep learning: concepts, CNN architectures, challenges, applications, future directions", vol. 8, no. 1. Springer International Publishing, 2021.

H. Naser, K. Al-behadili, and K. R. Ku-mahamud, "Hybrid K-Nearest Neighbour and Particle Swarm Optimization Technique for Divorce Classification," Int. J. Adv. Sci. Eng. Inf. Technol.vol. 11, no. 4, pp. 1447–1454, 2021.

X. Gao and G. Li, "A KNN Model Based on Manhattan Distance to Identify the SNARE Proteins," IEEE Access, vol. 8, pp. 112922–112931, 2020, doi: 10.1109/ACCESS.2020.3003086.

“Webots: robot simulator.†https://cyberbotics.com/#cyberbotics (accessed Aug. 24, 2021).

G. Belingardi, A. Valle, M. P. Cavatorta, and I. Singh, "Bilby Rover autonomous mobile robot in the context of Industry 4.0," PhD Thesis. Politecnico di Torino, 2021.

N. Ahmed and W. J. Teahan, "Using Compression to Discover Interesting Behaviours in a Hybrid Braitenberg Vehicle," IEEE Access, vol. 9, pp. 11316–11327, 2021, doi: 10.1109/ACCESS.2021.3050882.

Y. Wu, Y. Li, X. Ge, Y. Gao, and W. Qian, "An Efficient Method for Calculating the Error Statistics of Block-Based Approximate Adders," IEEE Trans. Comput., vol. 68, no. 1, pp. 21–38, 2019, doi: 10.1109/TC.2018.2859960.

T. S. Mahmoud, D. Habibi, M. Y. Hassan, and O. Bass, "Modelling self-optimised short term load forecasting for medium voltage loads using tunning fuzzy systems and Artificial Neural Networks," Energy Convers. Manag., vol. 106, no. December, pp. 1396–1408, 2015, doi: 10.1016/j.enconman.2015.10.066.

S. Nilwong, D. Hossain, S. I. Kaneko, and G. Capi, "Deep learning-based landmark detection for mobile robot outdoor localization," Machines, vol. 7, no. 2, 2019, doi: 10.3390/machines7020025.

L. Zhang, L. Wei, P. Shen, W. Wei, G. Zhu, and J. Song, "Semantic SLAM based on object detection and improved octomap," IEEE Access, vol. 6, no. c, pp. 75545–75559, 2018, doi: 10.1109/ACCESS.2018.2873617.

DOI: http://dx.doi.org/10.18517/ijaseit.12.4.16181


  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development