Deep learning for 6D pose estimation of objects — A case study for autonomous driving. (1st August 2023)

• Record Type: Journal Article
• Authors: Hoque, Sabera; Xu, Shuxiang; Maiti, Ananda; Wei, Yuchen; Arafat, Md. Yasir
• Is Part Of: Expert systems with applications. Volume 223(2023)
• Publication Date: 2023-08-01
• Subjects: Machine learning -- Deep neural network -- Computer vision -- Object detection -- Image processing -- CNN -- 3D object detection -- 6D pose estimation -- Autonomous vehicle; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2023.119838
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: Nowadays, the potential benefits and implementation of autonomous driving have attracted widespread attention from both industry and academia. This study will solve view-invariant object detection and semantic key-point pose assumptions from a single RGB image. A machine learning method for estimating the absolute pose of an on-road vehicle for autonomous driving from monocular vision alone without the help of additional sensors is a complex task. The main purpose of this work is to identify other vehicles on the road and estimate their exact angular position from a single image with improved accuracy. The focus of the study is to create a new algorithm by applying a potentially deep convoluted neural network followed by a repetitive neural structure for more accurate 6D pose inference. A 6D pose hypothesis is presented in this study, based on a deep hybrid architecture for individual vehicles of an end-to-end approach to a task consisting of a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN). In this work, we will use a large-scale dataset consistent with the understanding of a 3D car instance called ApolloCar3D. The data set contains 5, 277 real-life street scenes with examples of about 60K cars. By comparison, the ApolloCar3D is twenty times larger than the PASCAL3D+ and KITTI datasets. Ultimately, the idea is to efficiently eliminate motionless cars and predict the next pose given in the speed context, allowing a comprehensive evaluation, and passing the output through LSTM (long short-term memory) with an additional filter layer. The new filter added to the LSTM will efficiently filter and isolate stationary or parking vehicles and focus on on-road vehicles. Since the LSTM has a non-linear high-dimensional hidden memory state, it can preserve the past continuity of each generation's data history and pay more attention to those road vehicles rather than parked or stationary vehicles to act accordingly. So for each new vehicle, the pose estimator classifier can use LSTM memory and compare the historical pose with the newly filtered data. The successful implementation of this innovative concept will lead to significant improvements in the real-life traffic situation in the field of computer vision and autonomous driving.