Object detection and Autoencoder-based 6D pose estimation for highly cluttered Bin Picking
Bin picking is a core problem in industrial environments and robotics, with its main module as 6D pose estimation. However, industrial depth sensors have a lack of accuracy when it comes to small objects. Therefore, we propose a framework for pose estimation in highly cluttered scenes with small objects, which mainly relies on RGB data and makes use of depth information only for pose refinement. In this work, we compare synthetic data generation approaches for object detection and pose estimation and introduce a pose filtering algorithm that determines the most accurate estimated poses. We will make our
Code (0)
등록된 구현이 없습니다.
Tasks
6D Pose Estimationobject-detectionObject DetectionPose EstimationSynthetic Data GenerationSimilar Papers 제목 키워드 기반
GeneA-SLAM2: Dynamic SLAM with AutoEncoder-Preprocessed Genetic Keypoints Resampling and Depth Variance-Guided Dynamic Region Removal
Existing semantic SLAM in dynamic environments mainly identify dynamic regions through object detection or semantic segmentation methods. However, in certain highly dynamic scenarios, the detection boxes or segmentation …
object-detectionObject DetectionPose EstimationSemantic Segmentation+1Implicit 3D Orientation Learning for 6D Object Detection from RGB Images
We propose a real-time RGB-based pipeline for object detection and 6D pose estimation. Our novel 3D orientation estimation is based on a variant of the Denoising Autoencoder that is trained on simulated views of a 3D mod…
6D Pose Estimation6D Pose Estimation using RGBDenoisingObject+3Transformer autoencoder with local attention for sparse and irregular time series with application on risk estimation
This paper introduces a framework specifically designed for sparse and irregular time series {risk estimation}. It is based on a Transformer Autoencoder with local attention, which leverages the powerful pattern identifi…
Absolute distance prediction based on deep learning object detection and monocular depth estimation models
Determining the distance between the objects in a scene and the camera sensor from 2D images is feasible by estimating depth images using stereo cameras or 3D cameras. The outcome of depth estimation is relative distance…
Depth EstimationMonocular Depth Estimationobject-detectionObject DetectionDisentangling Latent Hands for Image Synthesis and Pose Estimation
Hand image synthesis and pose estimation from RGB images are both highly challenging tasks due to the large discrepancy between factors of variation ranging from image background content to camera viewpoint. To better an…
Image GenerationPose Estimation