An optimised extreme learning machine (OELM) for simultaneous localisation and mapping in autonomous vehicles

摘要

Autonomous robots can navigate unexpected situations without human involvement. Multiple sensors for object recognition and mapping are used in autonomous driving, factory automation, and security service robots. Advances in motion planning allow autonomous cars to map their position and orientation. Vehicle position estimates may be off. SLAM algorithms map an unfamiliar environment and estimate a vehicle's position. We present a grey wolf optimised extreme learning machine (OELM) approach to leverage deep learning networks. It can build an environment map independently and help the system navigate autonomously. First, a CNN-GRU hybrid model for training Stereo and IMU sensor data is provided. ELM is then optimised for SLAM. The proposed method minimises posture estimate error for autonomous vehicles. Our method captures autonomous driving scenarios using KITTI data. IMU and stereo images collect data. Comparing the computed path to ground truth poses enhances accuracy and decreases error. Improved ELM has lower RMSE than prior SLAM. Data show that OELM-SLAM is more accurate than ELM.