Robust and Accurate Multiple-Camera Pose Estimation toward Robotic Applications

Abstract

Pose estimation methods in robotics applications frequently suffer from inaccuracy due to a lack of correspondence and real-time constraints, and instability from a wide range of viewpoints, etc. In this paper, we present a novel approach for estimating the poses of all the cameras in a multi-camera system in which each camera is placed rigidly using only a few coplanar points simultaneously. Instead of solving the orientation and translation for the multi-camera system from the overlapping point correspondences among all the cameras directly, we employ homography, which can map image points with 3D coplanar-referenced points.In our method, we first establish the corresponding relations between each camera by their Euclidean geometries and optimize the homographies of the cameras; then, we solve the orientation and translation for the optimal homographies. The results from simulations and real case experiments show that our approach is accurate and robust for implementation in robotics applications. Finally, a practical implementation in a ping-pong robot is described in order to confirm the validity of our approach.

3D Mesh Compression and Transmission for Mobile Robotic Applications

“…3D mapping and reconstruction [2,3] produce structural 3D geometric representations for robot operating environments and their objects, providing comprehensive inputs for motion planning to enhance results and guide robot navigation and operations more appropriately. The accuracy of such processes depends on appropriate camera pose estimation [4]. One popular representation of such 3D mapping is meshes, which may entail large data size, causing performance issues in passing environment and object representations to system modules for 3D mapping, motion planning, and robot operation visualization.…”

Stereo Vision Guiding for the Autonomous Landing of Fixed-Wing UAVs: A Saliency-Inspired Approach

“…Stereo visual localization is inspired by the human vision system (HVS) and supported by stereo vision measurement theory, which has been involved in many applications [3]. During flight, aircrafts often move through a cluttered background (e.g., cloudy sky, forest, mountain, the ground).…”

A Sliding Mode Control-Based on a RBF Neural Network for Deburring Industry Robotic Systems

“…A quick attribute reduction algorithm for a neighbourhood rough set model is proposed in [30]. An efficient and robust pose estimation algorithm for multi-camera systems that can obtain 6DOF poses for all cameras using only a few coplanar points simultaneously is proposed in [31]. Detection chain repeatability, average detection chain reprojection error and matching chain precision are presented in [32].…”

“…Additional algorithms have also been proposed [29][30][31]. A subset selection algorithm considers the selected and remaining features; this approach can avoid the interference of relevant but redundant features and overcome the weakness of forward greedy search based methods [29].…”