6D Pose Estimation of Objects: Recent Technologies and Challenges
Source abstract
Abstract
6D pose estimation is a common and important task in industry. Obtaining the 6D pose of objects is the basis for many other functions such as bin picking, autopilot, etc. Therefore, many corresponding studies have been made in order to improve the accuracy and enlarge the range of application of various approaches. After several years of development, the methods of 6D pose estimation have been enriched and improved. Although some predecessors have analyzed the methods and summarized them in detailed, there have been many new breakthroughs in recent years. To understand 6D pose estimation better, this paper will make a new and more detailed review of 6D pose estimation. We divided these methods into two approaches: Learning-based approaches and non-learning-based approaches, including 2D-information-based approach and 3D-information-based approach. Additionally, we introduce the challenges that exist in 6D pose estimation. Finally, we compare the performance of different methods qualitatively and discuss the future development trends of the 6D pose estimation.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Robot Manipulation and Learning · Robotics and Sensor-Based Localization · Image and Object Detection Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical Engineering, the State Key Lab of Fluid Power & Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
Zhejiang Institute of Mechanical and Electrical Engineering, Hangzhou 310053, China
Funding
National Natural Science Foundation of China · Zhejiang Province Public Welfare Technology Application Research Project
References (94)
Show source-provided references
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