Pose Estimation and 3D Bounding Box Prediction for Autonomous Vehicles Through Lidar and Monocular Camera Sensor Fusion

Abstract

This thesis investigates the integration of transfer learning with ResNet-101 and compares its performance with VGG-19 for 3D object detection in autonomous vehicles. ResNet-101 is a deep Convolutional Neural Network with 101 layers and VGG-19 is a one with 19 layers. The research emphasizes the fusion of camera and lidar outputs to enhance the accuracy of 3D bounding box estimation, which is critical in occluded environments. Selecting an appropriate backbone for feature extraction is pivotal for achieving high detection accuracy. To address this challenge, we propose a method leveraging transfer learning with ResNet- 101, pretrained on large-scale image datasets, to improve feature extraction capabilities. The averaging technique is used on output of these sensors to get the final bounding box. The experimental results demonstrate that the ResNet-101 based model outperforms the VGG-19 based model in terms of accuracy and robustness. This study provides valuable insights into the effectiveness of transfer learning and multi-sensor fusion in advancing the innovation in 3D object detection for autonomous driving.