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Mini review Special Issues

Point cloud registration: a mini-review of current state, challenging issues and future directions

  • Received: 01 September 2022 Revised: 30 October 2022 Accepted: 08 December 2022 Published: 10 January 2023
  • A point cloud is a set of data points in space. Point cloud registration is the process of aligning two or more 3D point clouds collected from different locations of the same scene. Registration enables point cloud data to be transformed into a common coordinate system, forming an integrated dataset representing the scene surveyed. In addition to those reliant on targets being placed in the scene before data capture, there are various registration methods available that are based on using only the point cloud data captured. Until recently, cloud-to-cloud registration methods have generally been centered upon the use of a coarse-to-fine optimization strategy. The challenges and limitations inherent in this process have shaped the development of point cloud registration and the associated software tools over the past three decades. Based on the success of deep learning methods applied to imagery data, attempts at applying these approaches to point cloud datasets have received much attention. This study reviews and comments on more recent developments in point cloud registration without using any targets and explores remaining issues, based on which recommendations on potential future studies in this topic are made.

    Citation: Nathan Brightman, Lei Fan, Yang Zhao. Point cloud registration: a mini-review of current state, challenging issues and future directions[J]. AIMS Geosciences, 2023, 9(1): 68-85. doi: 10.3934/geosci.2023005

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  • A point cloud is a set of data points in space. Point cloud registration is the process of aligning two or more 3D point clouds collected from different locations of the same scene. Registration enables point cloud data to be transformed into a common coordinate system, forming an integrated dataset representing the scene surveyed. In addition to those reliant on targets being placed in the scene before data capture, there are various registration methods available that are based on using only the point cloud data captured. Until recently, cloud-to-cloud registration methods have generally been centered upon the use of a coarse-to-fine optimization strategy. The challenges and limitations inherent in this process have shaped the development of point cloud registration and the associated software tools over the past three decades. Based on the success of deep learning methods applied to imagery data, attempts at applying these approaches to point cloud datasets have received much attention. This study reviews and comments on more recent developments in point cloud registration without using any targets and explores remaining issues, based on which recommendations on potential future studies in this topic are made.



    The study of the dynamical behavior of dynamical systems is attracting a lot of research efforts from various fields. The existence of chaos is an interesting phenomenon associated with a nonlinear dynamical system. Various dynamics, the existence of chaos, control, and synchronization have been studied on a large number of dynamical systems. A dynamical system involving fractional time derivatives instead of integer order time derivatives is known as a fractional dynamical system [1,2,3,4,5,6]. The fractional-order system is a generalization of an integer order system. It has been extensively applied for the modeling of many real problems appearing in Viscoelastic systems [7], distributed-order dynamical systems [8], hydroturbine-governing systems [9], glucose-insulin regulatory systems [10] and so on.

    Stability, control and synchronization are three important concepts to investigate in chaotic fractional dynamical systems. In recent years, several methods of chaos synchronization have been proposed in the literature for fractional-order chaotic systems. For example, [11] investigated optimal synchronization of chaotic fractional systems. They used finite time synchronization for an optimal control problem. The authors [12] applied function cascade synchronization for the fractional-order chaotic systems. Mahmoud et al. [13] presented the idea of complex modified projective phase synchronization of the nonlinear chaotic system with complex variables. Aghababa [14] proposed a sliding mode technique for the goal of finite-time chaos control and synchronization of fractional-order nonautonomous chaotic systems. The adaptive fuzzy control scheme, sliding-mode control, linear, nonlinear, active, feedback, and adaptive control methods have been applied for the global stability and synchronization of chaotic fractional systems [15,16,17,18,19].

    Estimation of ultimate bound sets (UBSs) and global exponential attractive sets (GEAS) is an important topic in dynamical systems that is applied to the study of chaos control, chaos synchronization, Hausdorff dimension, and numerical search of hidden attractors [20,21,22,23,24]. In fact, if one can calculate an ultimate bound set (UBS) or globally attractive set (GAS) for a system, then the system cannot have chaotic attractors, equilibrium points, periodic solutions, quasi-periodic solutions, etc. outside the GAS [25,26]. This is very important for engineering applications, since it is very difficult to predict the existence of hidden attractors. Therefore, how to get the GASs of a chaotic dynamical system is particularly significant both for theoretical research and practical applications. In 1987, Leonov published the initial results of the global UBS for the Lorenz model [27,28,29]. After that, Swinnerton-Dyer [30] showed that the Lyapunov function can be used to study the bounds of the states of the Lorenz equations. This idea was developed by many researchers, and they were able to compute the GAS and PIS for different chaotic systems by constructing a family of generalized Lyapunov functions including general Lorenz system [31], complex Lorenz [32], financial risk [33], chaotic dynamical finance model [34], etc.

    Motivated by the above discussion, in this article, a five-dimensional fractional-order chaotic complex system is proposed. By means of theoretical analysis and numerical simulation, some basic dynamical properties, such as phase portraits, bifurcation diagram, Lyapunov exponents and chaos diagram of the presented system are studied with varying the fractional derivative orders. An interesting point that we investigated for this system is that varying the fractional-order parameter (α) causes the transition of the system from a chaotic state to a steady state. The UBS and GAS for the chaotic fractional systems have been estimated so far in two papers by Jian et al. [35,36]. Also, Jian et al. investigated the global Mittag-Leffler boundedness for fractional-order neural networks [37,38]. To the best of our knowledge, the GAS and the PIS for the fractional chaotic complex system have not been investigated. As an innovation, in addition to proving the global boundness of the proposed system, we calculate a family of GASs. In fact, by changing system parameters and other conditions, we can create a variety of attractive sets. The boundaries calculated for the complex chaotic system in this paper can be used for the purpose of global chaos synchronization via linear feedback controls.

    This article is organized in the following sections. Section 2 presents the chaotic complex system with the fractional-order derivative. In Section 3, we will study the Mittag-Leffler GASs of the fractional chaotic complex system. Section 4 discusses the global synchronization of two fractional chaotic systems. Conclusions are drawn in Section 5.

    In this section, we introduce a complex chaotic system with Caputo's fractional derivative. Some dynamical properties of the fractional order system, including equilibrium points, bifurcation diagrams, Lyapunov exponent spectra (LEs), and the largest Lyapunov exponent (LLE), will be presented.

    The complex chaotic system can be represented as follows [39,40]:

    dz1dt=σ(z2z1)+z2z3,dz2dt=rz1z1z3z2,dz3dt=12(¯z1z2+¯z2z1)βz3, (2.1)

    where z1 and z2 are complex variables, z1=x1+jx2,z2=x3+jx4,z3=x5,j=1,¯z1 and ¯z2 are the conjugates of z1 and z2. The imaginary part and the real part of the complex system (2.1) are separated, which can be expressed as follows :

    dx1dt=σ(x3x1)+x3x5,dx2dt=σ(x4x2)+x4x5,dx3dt=rx1x3x1x5,dx4dt=rx2x4x2x5,dx5dt=x1x3+x2x4βx5. (2.2)

    The authors [26,39,40] derived some properties of system (2.2), including Lyapunov exponents, chaotic attractor, and ultimate bound estimation.

    The definitions of the Caputo integral and derivative are expressed in the following.

    Definition 2.1. The fractional integral function X(t), is

    IαX(t)=1Γ(α)tt0(ts)α1X(s)ds,tt0, (2.3)

    where Γ(.) is the Gamma function:

    Γ(m)=0sm1esds. (2.4)

    Definition 2.2. The Caputo fractional-order derivative of function XCn([t0,+),R) is

    DαtX(t)=1Γ(nα)tt0(ts)nα1X(n)(s)ds. (2.5)

    Definition 2.3. The Mittag-Leffler function Ep,q(.) with two parameters is defined as

    Ep,q(m)=k=0mkΓ(kp+q), (2.6)

    where p>0,q>0, and m is a complex number. Obviously,

    Ep(m)=Ep,1(m),E0,1(m)=11m,E1,1(m)=em.

    Here, we consider the five-dimensional fractional-order system

    Dαtx1(t)=σ(x3x1)+x3x5,Dαtx2(t)=σ(x4x2)+x4x5,Dαtx3(t)=rx1x3x1x5,Dαtx4(t)=rx2x4x2x5,Dαtx5(t)=x1x3+x2x4βx5, (2.7)

    where σ,β, and r are parameters to be varied, and α(0,1] is the derivative order.

    The equilibrium points of the system (2.7) can be found by solving the equations

    Dαtxi=0,(i=1,2,,5).

    After solving this equation, one can get the equilibrium points as

    E0=(0,0,0,0,0),E=(mcosθ,msinθ,(rrm2β+m2)mcosθ,(rrm2β+m2)msinθ,rm2β+m2),

    where,

    m2=βdrd,d=rσ±(σ+r)24σ2.

    When the parameters are chosen as σ=35,β=83,r=25 and α=0.98, the system (2.7) is chaotic, as depicted in Figures 1 and 2.

    Figure 1.  Phase portrait for (2.7) with σ=35,β=83,r=25 and α=0.98.
    Figure 2.  The chaotic behavior of system (2.7) with σ=35,β=83,r=25 and α=0.98.

    For the fractional-order system (2.7) with σ=35,β=83,r=25 and α=0.9 in Figures 3 and 4, chaos is suppressed, which means the system is controlled.

    Figure 3.  Phase portraitr of system (2.7) with σ=35,β=83,r=25 and α=0.9.
    Figure 4.  The behavior of system (2.7) with σ=35,β=83,r=25 and α=0.9.

    For the values of the parameters σ=35,β=83, and r=25, Lyapunov exponents are shown in Figure 5. The values of Lyapunov exponents at the 500th second are L1=0.92, L2=0.0007, L3=0.0194, L3=0.0194, L4=3.5401, L5=3.6868.

    Figure 5.  Lyapunov exponent spectra for system (2.7) with σ=35,β=83,r=25 and α=0.98.

    By means of the bifurcation diagrams, Lyapunov exponent spectra (LEs) and the largest Lyapunov exponent (LLE), the dynamical properties of system (2.7) are studied. The bifurcation diagrams of the system with varying derivative orders are plotted, and the results are shown in Figure 6, where σ=35,β=83, and r=25. In Figure 6 derivative order α varies from 0.97 to 1 with step size of 0.005. When α<0.972, the system converges to a fixed point, where the LLE of the system is zero or negative. There is a periodic window when α(0.9941,0.9992). From Figure 6, it is clearly shown that the fractional-order complex system (2.7) is chaotic over most of the scope α(0.97,1), where the LLE of the system is positive.

    Figure 6.  Bifurcation diagram obtained on variation of α, keeping the values of other parameter values, with σ=35,β=83,r=25 and α(0.97,1).

    The derivative orders can change the bifurcation types and dynamics of the system. Indeed, the derivative order is a bifurcation of the fractional-order chaotic complex system.

    In this section, we will calculate the Mittag-Leffler GASs for the fractional-order of complex chaotic system (2.7). Let consider the following fractional-order system:

    DαtX=f(X),X(t0)=X0, (3.1)

    where XRn, f:RnRn is sufficiently smooth, and X(t,t0,X0) is the solution.

    Definition 3.1. [35] For a given Lyapunov function Vλ(t)=Vλ(X(t)) with λ>0, if there exist constants Lλ>0 and rλ>0 for all X0Rn such that

    Vλ(t)Lλ(Vλ(t0)Lλ)Eα(rλ(tt0)α),tt0, (3.2)

    for Vλ(X)>Lλ, then Uλ={X|Vλ(X(t))Lλ} is said to be the Mittag-Leffler GAS of system (3.1). If for any X0Uλ and any t>t0, X(t,t0,X0)Uλ, then Uλ is said to be a Mittag-Leffler PISs, where X=X(t),X0=X(t0).

    Lemma 3.1. [36] If X(t)R is a continuous and differentiable function, then

    Dα(X2(t))2X(t)Dα(X(t)). (3.3)

    Lemma 3.2. [36] For α(0,1) and constant κR, if a continuous function X(t) meets

    Dα(X(t))κX(t),t0, (3.4)

    then

    X(t)X(0)Eα(κtα),t0. (3.5)

    The following theorem investigated the Mittag-Leffler GASs and the Mittag-Leffler PISs of the system (2.7):

    Theorem 3.1. Let β>0,σ>0 and r>0. Define

    Uλ,μ={X(t)R5λx21+λx22+(λ+μ)x23+(λ+μ)x24+μ(x5(σ+r)λ+rμμ)2R2max}. (3.6)

    Then, Uλ,μ is the ultimate bound and positively invariant set of system (2.7), where

    R2max=β((σ+r)λ+rμ)2ημ (3.7)

    and η=min{1,σ,β}>0.

    Proof. Define the following generalized positively definite and radically unbounded Lyapunov function

    Vλ,μ(x1,x2,x3,x4,x5)=12λx21+12λx22+12(λ+μ)x23+12(λ+μ)x24+12μ(x5(σ+r)λ+rμμ)2, (3.8)

    where λ>0,μ>0.

    Computing the fractional derivative of Vλ,μ along the trajectory of system (2.7) and using Lemma 3.1, we have

    DαtVλ,μ(X(t))λx1Dαtx1+λx2Dαx2+(λ+μ)x3Dαtx3+(λ+μ)x4Dαtx4+μ(x5(σ+r)λ+rμμ)Dαtx5=λx1(σ(x3x1)+x3x5)+λx2(σ(x4x2)+x4x5)+(λ+μ)x3(rx1x3x1x5)+(λ+μ)x4(rx2x4x2x5)+μ(x5(σ+r)λ+rμμ)(x1x3+x2x4βx5)=λσx21λσx22(λ+μ)x23(λ+μ)x24μβx25+β((σ+r)λ+rμ)x5=12λσx2112λσx2212(λ+μ)x2312(λ+μ)x2412μβ(x5(σ+r)λ+rμμ)2+F(X),

    where,

    F(X)=12λσx2112λσx2212(λ+μ)x2312(λ+μ)x2412μβx25+β((σ+r)λ+rμ)22μ. (3.9)

    It is obvious that F(X)supXR5F(X)=lλ,μ=β((σ+r)λ+rμ)22μ. From there we have

    DαtVλ,μ(X(t))ηVλ,μ+lλ,μ, (3.10)

    i.e.,

    Dαt(Vλ,μ(t)lλ,μη)η(Vλ,μ(t)lλ,μη). (3.11)

    Based on Lemma 3.2, one can obtain

    Vλ,μ(t)lλ,μη(Vλ,μ(0)lλ,μη)Eα(ηtα),t0. (3.12)

    Based on Definition 3.1, from (3.12) we conclude that the ellipsoid Uλ,μ for β>0,σ>0 and r>0 is a Mittag-Leffler GAS and Mittag-Leffler PIS for the system (2.7). This completes the proof.

    By changing the values of λ and μ, we can achieve different bounded sets. An interesting property about the the fractional-order parameter is that the change of α leads to a change in the behavior of the dynamical system from a chaotic state to a steady state.

    (i) If we take λ=1,μ=1, then

    U1,1={(x1,x2,x3,x4,x5)|x21+x22+2x23+2x24+(x5(σ+2r))2β(σ+2r)2},

    is the Mittag-Leffler GAS of system (2.7).

    When σ=35,b=83, and r=25, we have

    U1,1={(x1,x2,x3,x4,x5)|x21+x22+2x23+2x24+(x585)2(138.8)2}.

    Figure 7 shows the chaotic attractors and the Mittag-Leffler GASs of the system (2.7) in the different spaces defined by U1,1, for σ=35,β=83,r=25, and α=0.98. Considering the value of α=0.95, the solutions of (2.7) change from chaotic to steady-state. In this case, we found that chaos does not exist in the nonlinear fractional-order model. The phase portraits and Mittag-Leffler GAS of system (2.7) are shown through Figure 8.

    Figure 7.  Mittag-Leffler GAS of the system (2.7) with σ=35,β=83,r=25, and α=0.98.
    Figure 8.  Mittag-Leffler GAS of the system (2.7) with σ=35,β=83,r=25, and α=0.95.

    (ii) Let us take λ=1,μ=2, and then we get that the set

    U1,2={(x1,x2,x3,x4,x5)|x21+x22+3x23+3x24+2(x5σ+3r2)2β(σ+3r)22},

    is the Mittag-Leffler GAS of system (2.7).

    When σ=35,b=83, and r=25, we have

    U1,2={(x1,x2,x3,x4,x5)|x21+x22+3x23+3x24+2(x555)2127.012}.

    Figure 9 shows the phase portraits and the Mittag-Leffler GAS of system (2.7) in the different spaces defined by U1,2.

    Figure 9.  Mittag-Leffler GAS of the system (2.7) with σ=35,β=83,r=25, and α=0.98.

    (iii) Let us take λ=2,μ=1, and then we get that the set

    U2,1={(x1,x2,x3,x4,x5)|2x21+x22+3x23+3x24+(x5(2σ+3r))2β(2σ+3r)2},

    is the Mittag-Leffler GAS of system (2.7).

    When σ=35,b=83,r=25, and α=0.98, we have

    U2,1={(x1,x2,x3,x4,x5)|2x21+2x22+3x23+3x24+(x5145)2236.72}.

    In this section, we study the global synchronization of the complex chaotic system via linear feedback control. Let us state the following two lemmas which are used to prove synchronization in the presented system.

    Lemma 4.1. [32] For any ϵ>0,aR,bR, the inequality 2abϵa2+1ϵb2 holds.

    Lemma 4.2. [32] For k>0,aR and bR, the inequality ka2+ab12ka2+12kb2 holds.

    Let system (2.7) be the drive system with the response system

    Dαty1(t)=σ(y3y1)+y3y5+u1,Dαty2(t)=σ(y4y2)+y4y5+u2,Dαty3(t)=ry1y3y1y5+u3,Dαty4(t)=ry2y4y2y5+u4,Dαty5(t)=y1y3+y2y4βy5+u5, (4.1)

    where y1,y2,,y5 are state variables and u1,u2,,u5 are controllers to be designed so as to achieve global chaos synchronization between systems (4.1) and (2.7). From (3.6) in Theorem 3.1, we have

    |x1|Rmaxλ,|x2|Rmaxλ,|x3|Rmaxλ+μ,|x4|Rmaxλ+μ,|x5θ|Rmaxμ.

    Therefore, we can get the maximum boundness of states as the following:

    M1=Rmaxλ,M2=Rmaxλ,M3=Rmaxλ+μ,M4=Rmaxλ+μ,M5=Rmaxμ+θ,

    where θ=(σ+r)λ+rμμ. Then, we have the following theorem.

    Theorem 4.1. The global synchronization between the drive system (2.7) and response system (4.1) will occur via the control laws

    u1=u2=u5=0,u3=k3e3,u4=k4e4, (4.2)

    where

    k3>σ+2r+M52ϵ+M122,k4>σ+2r+M52ϵ+M222,0<ϵ<σσ+2r+M5+θ.\\

    Proof. Let the state errors be ei=yixi,i=1,2,,5. By subtracting (2.7) from (4.1), we obtain the error dynamical system:

    Dαte1(t)=σ(e3e1)+e3e5+e3x5+e5x3,Dαte2(t)=σ(e4e2)+e4e5+e4x5+e5x4,Dαte3(t)=re1e3e1e5e1x5e5x1k3e3,Dαte4(t)=re2e4e2e5e2x5e5x2k4e4,Dαte5(t)=e1e3+e1x3+e3x1+e2e4+e2x4+e4x2βe5. (4.3)

    Let V(e)=12e21+12e22+e23+e24+12e25. Based on Lemma 3.1, the fractional-order derivative of V along the system (4.3) is

    DαtV(e)e1Dαte1+e2Dαte2+2e3Dαte3+2e4Dαte4+e5Dαte5=e1(σ(e3e1)+e3e5+e3x5+e5x3)+e2(σ(e4e2)+e4e5+e4x5+e5x4)+2e3(re1e3e1e5e1x5e5x1k3e3)+2e4(re2e4e2e5e2x5e5x2k4e4)+e5(e1e3+e1x3+e3x1+e2e4+e2x4+e4x2βe5)=σe21σe22(2+k3)e23(2+k4)e24βe25+(σ+2r)e1e3+(σ+2r)e2e4+2e1e5x3+2e2e5x4e3e5x1e4e5x2e2e4x5e1e3x5.

    From Lemmas 4.1 and 4.2, we have

    (σ+2rx5)e1e3(σ+2r+M5+θ)|e1||e3|ϵ2(σ+2r+M5+θ)e21+12ϵ(σ+2r+M5+θ)e23,(σ+2rx5)e2e4(σ+2r+M5+θ)|e2||e4|ϵ2(σ+2r+M5+θ)e22+12ϵ(σ+2r+M5+θ)e24,σe21+2e1e5x3σe21+2M3|e1||e5|12σe21+2σM23e25,σe22+2e2e5x4σe22+2M4|e2||e5|12σe22+2σM24e25,e4e5x2e3e5x1M2|e4||e5|+M1|e3||e5|M22(e24+e25)+M12(e23+e25).

    Then,

    DαtV(e)12(σσϵ2rϵϵM5ϵθ)e2112(σσϵ2rϵϵM5ϵθ)e22(2+k3σ+2r+M52ϵM12)e23(2+k4σ+2r+M52ϵM22)e2412(2β4σM234σM24M1M2)e25.

    Set

    η1=σσϵ2rϵϵM5ϵθ,η2=σσϵ2rϵϵM5ϵθ,η3=2+k3σ+2r+M52ϵM12,η4=2+k4σ+2r+M52ϵM22,η5=2β4σM234σM24M1M2.

    Then, DαtV(e)ηV, where η=min{η1,η2,η3,η4,η5}. By Lemma 3.2, one can obtain V(t)V(0)Eα(ηtα),t0; and thus the error system (4.3) is global stable at e=0, implying the global Mittag-Leffler synchronization of trajectories in fractional-order systems (2.7) and (4.1) by control laws (4.2).

    To check the correctness of the presented theory for synchronization, we used simulation using MATLAB version 2021. The parameters of both systems are taken as σ=35,β=83,r=43, and α=0.98. The initial values of the master and slave systems are (1,1,2,1,1) and (0,0.1,1,0,0). Controllers (4.2) with k3=150, (4.2) and k4=160, are chosen, and then response system (4.1) synchronizes with drive system (2.7), as shown in Figures 10. Figure 11, shows synchronization errors between systems (2.7) and (4.1). From these figures, it is evident that the response trajectories fully converge to the drive, and the synchronization errors ei=yixi for i=1,2,3,4,5 converge to zero.

    Figure 10.  State trajectories of the drive-response systems for the fractional-order chaotic complex system.
    Figure 11.  The synchronization errors of fractional-order chaotic complex system.

    In this paper, we introduced the fractional-order chaotic complex system. Using the Lyapunov function and fractional-order derivative, the Mittag-Leffler GASs and Mittag-Leffler PISs for this system are obtained. Furthermore, we investigated some dynamical properties of the system, including phase portraits, bifurcation diagrams, and the Lyapunov exponents. Finally, based on the Lyapunov theory, we designed a linear feedback control to synchronize the two chaotic complex systems. Simulation results are given to show the validity of the proposed schemes.

    This work was supported by the National Science and Technology Council of Republic of China under contract NSTC 111-2622-B-214-001.

    The authors declare that there is no conflict of interests regarding the publication of this article.



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