Abstract
This paper introduces a dual-layer density estimation-based architecture for multiple object instance detection in robot inventory management applications. The approach consists of raw scale-invariant feature transform (SIFT) feature matching and key point projection. The dominant scale ratio and a reference clustering threshold are estimated using the first layer of the density estimation. A cascade of filters is applied after feature template reconstruction and refined feature matching to eliminate false matches. Before the second layer of density estimation, the adaptive threshold is finalized by multiplying an empirical coefficient for the reference value. The coefficient is identified experimentally. Adaptive threshold-based grid voting is applied to find all candidate object instances. Error detection is eliminated using final geometric verification in accordance with Random Sample Consensus (RANSAC). The detection results of the proposed approach are evaluated on a self-built dataset collected in a supermarket. The results demonstrate that the approach provides high robustness and low latency for inventory management application.
Highlights
With the development of robotics, humanoid robots have been introduced in innumerable applications
We focus on the goal of multiple object instance detection for robot inventory management and propose an effective approach to achieve this goal
The contributions of our work are as follows: (i) We propose an architecture for multiple object instance detection based on dual-layer density estimation
Summary
With the development of robotics, humanoid robots have been introduced in innumerable applications. Among the available functionalities of the humanoid robot, specific object detection has attracted increasing attention in recent years. Inventory management, autosorting, and pick-andplace system are typical applications. Unlike single-object detection, multiple-instance detection is a more challenging task. We focus on the goal of multiple object instance detection for robot inventory management and propose an effective approach to achieve this goal. Multiple object instance detection is a complex technology that encounters a variety of difficulties. Diversities of species, shapes, colors, and sizes of objects make it difficult to accomplish the fixed goal. Target objects appear different in different environments. Changes in scale, orientation and illumination increase uncertainty and ambiguity for identification. Multiple instances can affect the verification procedure
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