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Real-time Recognition and Pursuit in Robots Based on 3D Depth Data

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Abstract

In this work, we address the problem of robot pursuit based on a real-time object recognition system with 3D depth sensors. Compared with traditional RGBD data based recognition approaches, we propose a novel global online descriptor designed for object recognition from solely depth data. Proposed descriptor, which we name as Differential Histogram of Normal Vectors (DHONV), is designed to extract the geometric characteristics of the captured 3D surfaces of the objects presented in depth images. In order to obtain a brief description of the visible 3D surfaces of each object, we quantize the differential angles of the surface’s normal vectors into a 1D histogram. The object recognition experiments on a self-collected dataset and a benchmark RGB-D object dataset show that our proposed descriptor outperforms other depth data based descriptors. Moreover, we conducted real-time experiments with RoboCars. Our experiments with RoboCars validate our proposed method capability to perform a real-time recognition and pursuit tasks within indoor environment based solely on depth data.

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Authors and Affiliations

  1. Department of Advanced Science and Technology, Toyota Technological Institute, Nagoya, Japan

    Somar Boubou, Hamed Jabbari Asl, Tatsuo Narikiyo & Michihiro Kawanishi

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  1. Somar Boubou
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  2. Hamed Jabbari Asl
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  3. Tatsuo Narikiyo
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  4. Michihiro Kawanishi
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Corresponding author

Correspondence to Somar Boubou.

Appendix

Appendix

Here, we show how the kinematic models (13) and (14) are derived. Differentiating (8), we have

$$ \dot{\rho}=\frac{\left( \dot{x}_{2}-\dot{x}_{1}\right)\left( x_{2}-x_{1}\right)+\left( \dot{y}_{2}-\dot{y}_{1}\right)\left( y_{2}-y_{1}\right)}{\rho} $$
(21)

Also, from the definition of λ we have

$$\begin{array}{@{}rcl@{}} &&\rho\cos(\lambda)=x_{2}-x_{1} \end{array} $$
(22)
$$\begin{array}{@{}rcl@{}} &&\rho\sin(\lambda)=y_{2}-y_{1} \end{array} $$
(23)

Substituting these terms in Eq. 21, we obtain

$$\begin{array}{@{}rcl@{}} \dot{\rho}&=&-\nu_{1}\Big(\cos(\theta_{1})\cos(\lambda)+\sin(\theta_{1})\sin(\lambda)\Big)\\&+&\nu_{2}\Big(\cos(\theta_{2})\cos(\lambda)+\sin(\theta_{2})\sin(\lambda)\Big) \\&=&-\nu_{1}\cos(\theta_{1}-\lambda)+{\Delta}_{\rho} \end{array} $$
(24)

where \({\Delta }_{\rho }\triangleq \nu _{2}\cos (\theta _{2}-\lambda )\). Therefore, according to Eq. 9, the kinematics of ρ can be written as in Eq. 13.

Also, differentiating (10) and using Eqs. 22 and 23, we have

$$\begin{array}{@{}rcl@{}} \dot{\lambda} &=&\frac{\left( \sin(\theta_{2})\nu_{2}-\sin(\theta_{1})\nu_{1}\right)\cos(\lambda)-\left( \cos(\theta_{2})\nu_{2}-\cos(\theta_{1})\nu_{1}\right)\sin(\lambda)}{\rho}\\ &=&-\frac{\nu_{1}}{\rho}\sin(\theta_{1}-\lambda)-{\Delta}_{\psi} \end{array} $$
(25)

where \({\Delta }_{\psi }\triangleq -\frac {\nu _{2}}{\rho }\sin (\theta _{2}-\lambda )\). Therefore, differentiating (9) and using Eqs. 7 and 25, the kinematics of ψ can be written as in Eq. 14.

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Boubou, S., Jabbari Asl, H., Narikiyo, T. et al. Real-time Recognition and Pursuit in Robots Based on 3D Depth Data. J Intell Robot Syst 93, 587–600 (2019). https://doi.org/10.1007/s10846-017-0769-1

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