Dynamic categorization of 3D objects for mobile service robots

Abstract

Purpose This paper aims to present an object detection methodology to categorize 3D object models in an efficient manner. The authors propose a dynamically generated hierarchical architecture to compute very fast objects’ 3D pose for mobile service robots to grasp them. Design/methodology/approach The methodology used in this study is based on a dynamic pyramid search and fast template representation, metadata and context-free grammars. In the experiments, the authors use an omnidirectional KUKA mobile manipulator equipped with an RGBD camera, to localize objects requested by humans. The proposed architecture is based on efficient object detection and visual servoing. In the experiments, the robot successfully finds 3D poses. The present proposal is not restricted to specific robots or objects and can grow as much as needed. Findings The authors present the dynamic categorization using context-free grammars and 3D object detection, and through several experiments, the authors perform a proof of concept. The authors obtained promising results, showing that their methods can scale to more complex scenes and they can be used in future applications in real-world scenarios where mobile robot are needed in areas such as service robots or industry in general. Research limitations/implications The experiments were carried out using a mobile KUKA youBot. Scalability and more robust algorithms will improve the present proposal. In the first stage, the authors carried out an experimental validation. Practical implications The current proposal describes a scalable architecture, where more agents can be added or reprogrammed to handle more complicated tasks. Originality/value The main contribution of this study resides in the dynamic categorization scheme for fast detection of 3D objects, and the issues and experiments carried out to test the viability of the methods. Usually, state-of-the-art treats categories as rigid and make static queries to datasets. In the present approach, there are no fixed categories and they are created and combined on the fly to speed up detection.

Application of an Improved YOLOv5 Algorithm in Real-Time Detection of Foreign Objects by Ground Penetrating Radar

“…In addition, when PANet performs feature fusion, it only adds different input features directly, which will lead to unbalanced output features, as shown in Figure 11a. In this study, BiFPN was used to replace the FPN + PANet network [34] of the original YOLOv5, which improved its ability in multi-scale target recognition and the recognition rate of small targets in GPR images without increasing the computational cost [35]. BiFPN uses a fast normalized fusion algorithm, which gives different weights to the features of each layer for fusion so that the network pays more attention to important layers and reduces the node connections of some unnecessary layers.…”