Mobile robot localization using visual odometry in indoor environments with TurtleBot4 - Singh, Gurpreet; Goyal, Deepam; Kumar, Vijay (IAES International Journal of Artificial Intelligence (IJ-AI), 2025)
Accurate localization is crucial for mobile robots to navigate autonomously in indoor environments. This article presents a novel visual odometry (VO) approach for localizing a TurtleBot4 mobile robot in indoor settings using only an onboard red green blue – depth (RGB-D) camera. Motivated by the challenges posed by slippery floors and the limitations of traditional wheel odometry, an attempt has been made to develop a reliable, accurate, and low-cost localization solution. The present method extracts oriented FAST and rotated BRIEF (ORB) features for feature extraction and matching using brute-force matching with Hamming distance. The essential matrix is then computed using the 5-point algorithm and decomposed to recover the relative rotation and translation between poses. The absolute pose is obtained by chaining the incremental motions estimated from VO. Through experimentation and comparison with wheel odometry, the findings demonstrate the effectiveness of our VO system, achieving a positional accuracy with minimal error of 4-5%. The article also compares VO with wheel odometry and shows the advantages of using a visual approach, especially in environments with slippery floors where wheel slippage causes large odometry errors. Overall, this work presents an effective VO system for reliable, accurate, and low-cost localization of TurtleBot4 in indoor environments without relying on external infrastructure.