Dual‐Discriminator Conditional GAN and Optimized Binarized Spiking Neural Networks for Accurate Outdoor Robot Localization and Landmark Detection

ABSTRACT

Robot localization is the process of positioning a mobile robot in relation to its surroundings, and its precise positioning is essential for decision‐making and navigation. An autonomous robot's core competency is localization because the robot's positioning is significant in deciding its future decisions and navigation, but existing techniques lack accuracy in outdoor environments. To overcome this, in this manuscript, Dual‐Discriminator Conditional GAN and Optimized Binarized Spiking Neural Networks for Accurate Outdoor Robot Localization and landmark detection (DGAN‐LD‐OBNL) are proposed. This work addresses the critical need for precise positioning of robots in various applications by leveraging two novel deep learning techniques and a transfer learning technique. Here, outdoor robot localization data are collected from the virtual KITTI data set. Afterwards, Dual‐discriminator Conditional Generative Adversarial Network is proposed for precise landmark detection that significantly improves the localization accuracy. Then, Binarized Spiking Neural Network with EffiectiveNetV2 (BSNN‐ENetV2) is proposed to determine the robot location and compass direction. The weight parameters of BSNN‐ENetV2 are optimized using Volcano Eruption Optimization Algorithm for enhancing detection accuracy. The experimental outcomes show that the proposed method outperforms existing techniques in both accuracy, computational time, thus making it a promising solution for autonomous robot localization in outdoor environments.