Enhancing Autonomous Vehicle Perception: A 6-DOF Pose Estimation Framework Using Deep Learning
Year: 2024
Type: conference-paper
Abstract: 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.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4404954312