A Hierarchical Approach to Active Pose Estimation
Jascha Hellwig, Mark Baierl, Joao Carvalho, Julen Urain, Jan Peters

TL;DR
This paper introduces a hierarchical method for active pose estimation that combines quick image-based rough localization with detailed point cloud analysis, improving efficiency in robotic object search tasks.
Contribution
The paper presents a novel hierarchical approach that integrates RGB image analysis with point cloud data for more efficient active pose estimation in robotics.
Findings
Image feature processing accelerates search and pose estimation.
Hierarchical approach improves computational efficiency.
Method effectively handles occlusions in object localization.
Abstract
Creating mobile robots which are able to find and manipulate objects in large environments is an active topic of research. These robots not only need to be capable of searching for specific objects but also to estimate their poses often relying on environment observations, which is even more difficult in the presence of occlusions. Therefore, to tackle this problem we propose a simple hierarchical approach to estimate the pose of a desired object. An Active Visual Search module operating with RGB images first obtains a rough estimation of the object 2D pose, followed by a more computationally expensive Active Pose Estimation module using point cloud data. We empirically show that processing image features to obtain a richer observation speeds up the search and pose estimation computations, in comparison to a binary decision that indicates whether the object is or not in the current…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRobotics and Sensor-Based Localization · Image Processing and 3D Reconstruction · Image and Object Detection Techniques
