Enhancing Autonomous Vehicle Perception: A 6-DOF Pose Estimation Framework Using Deep Learning

This research addresses the critical challenge of precise perception of surrounding vehicles' positions and orientations in autonomous driving environments. We aim to develop an algorithm to estimate the absolute 6-degree-of-freedom (6-DOF) pose of vehicles from single images using a dataset of over 60,000 labeled 3D vehicle instances. Our approach integrates Faster R-CNN for 2D bounding box detection, Q-Net for 6D-pose regression, and DenseNet for enhanced feature extraction. Data analysis revealed attribution errors in pitch and yaw and significant variance in spatial accuracy across different vehicle models. By fine-tuning the Faster R-CNN on BDD100k and our dataset, and employing weighted bounding box and point fusion techniques, our model outperforms existing methods in accuracy and robustness. This work provides a reliable method for autonomous vehicles to perceive their surroundings accurately, enhancing navigation safety and efficiency.