Abstract
The depth estimation of the 3D deformable object has become increasingly crucial to various intelligent applications. In this paper, we propose a feature-based approach for accurate depth estimation of a deformable 3D object with a single camera, which reduces the problem of depth estimation to a pose estimation problem. The proposed method needs to reconstruct the target object at the very beginning. With the 3D reconstruction as an a priori model, only one monocular image is required afterwards to estimate the target object’s depth accurately, regardless of pose changes or deformability of the object. Experiments are taken on an NAO robot and a human to evaluate the depth estimation accuracy by the proposed method.
Highlights
In the case of human–robot cooperation, deep reinforcement learning (DRL) is used to train the robot to undertake the task
In order to train the capability of avoiding obstacles for the robot through DRL method, one has to prepare a huge number of samples, which are usually hard to generate
We reconstruct the NAO robot to test the depth accuracy by the proposed algorithm. This experiment is undertaken on a single monocular camera to reconstruct the NAO robot in its dynamic status
Summary
In the case of human–robot cooperation, deep reinforcement learning (DRL) is used to train the robot to undertake the task. For an instance of the bolt screwing task, the human partner’s arms might be obstacles for the robot during the working process (Figure 1). In order to train the capability of avoiding obstacles for the robot through DRL method, one has to prepare a huge number of samples, which are usually hard to generate. In this case, it can be done by reconstructing the 3D imaging [1,2,3]. The reconstruction sequence of the human’s arms can be used as moving obstacles to train the obstacle avoidance capability of the robot in a virtual environment (Figure 2). When an object is projected onto the camera plane, its depth information along the optical axis is lost, which possibly makes two far separated objects look close to each other [4]
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