6D pose annotation and pose estimation method for weak-corner objects under low-light conditions

In unstructured environments such as disaster sites and mine tunnels,it is a challenge for robots to estimate the poses of objects under complex lighting backgrounds,which limit their operation.Owing to the shadows produced by a point light source,the brightness of the operation scene is seriously unbalanced,and it is difficult to accurately extract the features of objects.It is especially difficult to accurately label the poses of objects with weak corners and textures.This study proposes an automatic pose annotation method for such objects,which combine 3D-2D matching projection and rendering technology to improve the efficiency of dataset annotation.A 6D object pose estimation method under low-light conditions(LP_TGC)is then proposed,including(1)a light preprocessing neural network model based on a low-light preprocessing module(LPM)to balance the brightness of a picture and improve its quality;and(2)a 6D pose estimation model(TGC)based on the keypoint matching.Four typical datasets are constructed to verify our method,the experimental results validated and demonstrated the effectiveness of the proposed LP_TGC method.The estimation model based on the preprocessed image can accurately estimate the pose of the object in the mentioned unstructured environments,and it can improve the accuracy by an average of~3%based on the ADD metric.

Multi-view space object recognition and pose estimation based on kernel regression

The application of high-performance imaging sensors in space-based space surveillance systems makes it possible to recognize space objects and estimate their poses using vision-based methods. In this paper, we proposed a kernel regression-based method for joint multi-view space object recognition and pose estimation. We built a new simulated satellite image dataset named BUAA-SID 1.5 to test our method using different image representations. We evaluated our method for recognition-only tasks, pose estimation-only tasks, and joint recognition and pose estimation tasks. Experimental results show that our method outperforms the state-of-the-arts in space object recognition, and can recognize space objects and estimate their poses effectively and robustly against noise and lighting conditions.

Object recognition and pose estimation using appearance manifolds

Conventionally, image object recognition and pose estimation are two independent components in machine vision. This paper presented a simple but effective method KNNSNG, which tightly couples these two com ponents within a single algorithm framework. The basic idea of this method came from the bionic pattern recog nition and the manifold ways of perception. Firstly, the shortest neighborhood graphs （SNG） are established for each registered object. SNG can be regarded as a covering and triangulation for a hypersurface on which the training data are distributed. Then for recognition task, the deter mined test image lies on which SNG by employing the parameter ＂k＂, which can be calculated adaptively. Finally, the local linear approximation method is adopted to build a local map between highdimensional image space and lowdimensional manifold for pose estimation. The projective coordinates on manifold can depict the pose of object. Experiment results manifested the effectiveness of the method.