Review On Odometry Techniques in Robotics Applications

  • Isra Arshad
  • Merlin Divya A
  • Shriya P
Keywords: Odometry, Autonomous Navigation, Motion Tracking, Visual Odometry, Feature-Based Approach, Localization Sensors.


With speedy advancements within the location of robotics and automation, a developing want has arisen in the direction of accurate navigation and localization of transferring gadgets. Modern sensors and algorithms are required for shifting robots with the capability to understand their environment, and enable the deployment of novel localization schemes, which include odometry, or Simultaneous Localization and Mapping (SLAM). For self sufficient navigation, movement tracking, and obstacle detection and avoidance, a robotic need to preserve information of its function over the years. Imaginative and prescient-based totally odometry is a sturdy approach applied for this cause. It lets in a automobile to localize itself robustly with the aid of the usage of best a movement of pix captured through a camera connected to the vehicle. This paper presents a top level view of present day odometry techniques, packages, and demanding situations in cell robots. The observe offers a comparative evaluation of different techniques and algorithms related to odometry and emphasizing on its efficiency and different characteristic extraction functionality, and programs. In this paper we have done a rigorous literature survey on odometry techniques and presented it in a proper format.


[1] Shashi Poddar1, Rahul Kottath2, Vinod Karar3 based on Evolution of Visual Odometry Techniques, April 2018
[2] Mohammad O. A. Aqel1, Mohammad H. Marhaban2, M. Iqbal Saripan3, Napsiah Bt. Ismail4 based on Review of visual odometry: types, Approaches, Challenges and techniques, Aqel et al. SpringerPlus (2016) 5:1897.
[3] Ke Wang, Member1, Sai Ma2, Junlan Chen3, Fan Ren4, Jianbo Lu5, Fellow6 based on Approaches Challenges and Applications for Deep Visual Odometry: Toward to Complicated and Emerging Areas, IEEE JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015.
[4] Vikas Thapa1, Abhishek Sharma2, Beena Gairola3, Amit K. Mondal4, Vindhya Devalla5, Ravi K. Patel6 based on Visual Odometry Techniques for Mobile Robots: Types and Challenges, 2018 QSTP, Doha 210531, Qatar.
[5] Davide Scaramuzza1, Friedrich Fraundorfer2 based on Visual Odometry, IEEE Robotics & Automation Magazine ( Volume: 18, Issue: 4, December 2011).
[6] Shashi Poddar1, Rahul Kottath2, Vinod Karar3 based on Evolution of Visual Odometry Techniques, arXiv:1804.11142, 2018.
[7] Mengshen Yang1, Xu Sun2 ,Fuhua Jia3, Adam Rushworth4, Xin Dong5, Sheng Zhang6, Zaojun Fang7, Guilin Yang8, Bingjian Liu9 based on Sensors and Sensor Fusion Methodologies for Indoor Odometry, Polymers 2022, 14(10), 2019.
[8] Yaxuan Yan1, Baohua Zhang2, Jun Zhou3, Yibo Zhang4, Xiao’ang Liu5 based on Real-Time Localization and Mapping Utilizing Multi-Sensor Fusion and Visual–IMU–Wheel Odometry for Agricultural Robots in Unstructured, Dynamic and GPS-Denied Greenhouse Environments, Agronomy 2022, 12(8), 1740.
[9] Mert Gurturk1, Abdullah Yusefi2 ,Muhammet Fatih Aslan3, Metin Soycan4, Akif Durdu5,Andrea Masiero6 based on The YTU dataset and recurrent neural network based visual-inertial odometry, ELSEVIER Measurement Volume 184, November 2021, 109878.
[10] Julian Nubert1, Shehryar Khattak2, Marco Hutter3 based on Self-supervised Learning of LiDAR Odometry for Robotic Applications, 2021 IEEE International Conference on Robotics and Automation (ICRA).
How to Cite
Arshad, I., Divya A, M., & P, S. (2023). Review On Odometry Techniques in Robotics Applications. Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146, 9(2), 67-69.

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.