On the minimization over SO(3) Manifolds


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1 On the minimization over SO(3) Manifolds Frank O. Kuehnel RIACS NASA Ames Research Center, MS 69, Moffett Field, CA 9435, USA Abstract. In almost all imagemodel and modelmodel registration problems the question arises as to what optimal rigid body transformation applies to bring a physical 3dimensional model in alignment with the observed one. Data may also be corrupted by noise. Here I will present the exponential and quaternion representations for the SO(3) group. I will present the technique of compounding derivatives and demonstrate that it is most suited for dealing with numerical optimization problems that involve rotation groups. 1 Introduction A common task in computer vision is matching images or features and estimating essential transformation parameters [1]. In the weak perspective regime the dimensional affine image transformation with 6 parameters is applicable; in general a perspective transformation applies. Amongst Fig. 1. Flyby satellite view on a rendered region of duckwater. The main difference between both images is a rotation about the viewing axis. those parameters are 3 Eulerangles that describe the orientation of the
2 viewer with respect to a worldcoordinate system, see Fig. 1 as an example. Then neglecting other free parameters, we could formulate the image matching problem as finding a minimum to the log likelihood log P p (I p Îp(φ,θ,ψ)), (1) where we sum over all pixels in the observed image data I p and the expected image Îp. It is well known that numerical optimiztion algorithms with Eulerangle representation have numerical problems. Several alternatives to Eulerangle representations exist. However, a prior it is not clear how well various representations and minimization methods will perfom. The subject of my investigation is the types of suitable rotation group representations and their application in numerical minimization algorithms. I will compare the convergence rate of various methods in the special case of matching pointclouds. In the next section, I will present suitable rotation group representations, followed by their application within numerical optimization algorithms. SO(3) reprsentations I briefly introduce the euler, the exponential and the quaternion representation. An extensive introduction to rotation groups and parametrization can be found in []..1 Eulerangle representation The rotation matrix R represents an orthonormal transformation, RR T = I, det(r) = 1, as such it can be decomposed into simpler rotation matrices, R = R z (φ) R y (θ)r z (ψ), () with cos φ sinφ cos θ sin θ R z (φ) = sinφ cos φ, R y (θ) = 1. (3) 1 sinθ cos θ The decomposition () is not unique. We adhere to the NASA Standard Aerospace convention [3] with ψ the precession, θ the nutation and φ as the spin. The derivatives with respect to the Eulerangles are easily obtained and will not be given here.
3 . Exponential representation, (Axisangle) The axisangle representation is frequently used in kinematics [4] and commonly referred to as the exponential representation. The rotation matrix R is obtained by exponentiation of a matrix H. This generating matrix H must be antisymmetric. and in 3dimensions constitutes the cross product operator, r z r y H = [r] = r z r x, r = ω/θ,θ = ω, (4) r y r x where ω is called the Rodrigues vector. We notice that in the limit θ the Rodrigues vector is illdefined and we need proper limit considerations [5]. The rotation matrix R and its generating matrix H are related by, R = exp(θh) = I + sin θh + (1 cosθ)h. (5) In minimization problems that utilize gradients we require the knowledge of derivatives with respect to the components of the Rodrigues vector ω xyz. In the following I introduce the index notation: Greek indices run over {1,, 3} which is synonymous for {x, y, z}, repeated indices are implicitly summed over 1. Now the derivatives are easily obtained dr = sin θ dh dr β + (1 cos θ) dh dr β + cos θhr α + sin θh r α, (6) dω α dr β dω α dr β dω α where Hence, using and dr β dω α = (δ αβ r α r β )/θ, dθ dω α = r α. (7) dh dr x r x + dh dr y r y + dh dr z r z = H (8) dh dr x r x + dh dr y r y + dh dr z r z = H (9) we obtain the rotation matrix derivative with respect to the Rodrigues vector component dr = dh sin θ dω α dr α θ ( + dh (1 cos θ) + dr α θ H(cos θ sin θ/θ) + H (sin θ 1 cos θ θ 1 summation over recurrent indicies is call Einstein summation ) ) r α. (1)
4 . It is emphasized that the natural endowed algebra sturcture of the vector space ω IR 3 is not isomorphic to the multiplication in the SO(3) group, R(ω 1 ) R(ω ) R(ω 1 + ω )! (11).3 Quaternion representation Quaternions can compactly represent rotation matrices. We will see that the compounding (noncommutative multiplication) operation is isomorphic to the matrix multiplication in SO(3). Let s introduce general quaternions by ˆQ = (q,q), q = q 1 e 1 + q e + q 3 e 3, (1) where q is a regular 3dimensional vector with basis {e α } in IR 3. Addition of quaternions is componentwise, a product (compounding) of two quaternions as follows, ˆP ˆQ = (p q p T q,p q + q p + p q). (13) The notation in (13) utilizes the dot and cross product defined in IR 3. It is remarked that the product (13) is noncommutative but associative! In addition we have a conjugate operation, hence we can write the squared norm as, ˆQ = (q,q) ˆQ = (q, q), (14) ˆQ ˆQ = (q + q T q,) q + q T q. (15) We identify a scalar value c IR with a quaternion that has zero vector components, (c,) c and a vector q IR 3 with a quaternion that has zero scalar component (, q) q. Further, we will be sloppy with the quaternion product notation but implicitly assume that ˆQ ˆP means ˆQ ˆP. The claim is that a rotation can be represented by ˆR = (cos θ,sin θ r), ˆR ˆR 1 (16) with θ the magnitude and r the direction of the Rodirigues vector given in (4) and below, such that Rv = ˆRˆV ˆR, ˆV = (,v). (17)
5 Proof of (17) is easy and proceeds as follows: Writing equation (5) in the form with halfangles, Rv = cos θ Iv + cos θ sin θ r v + sin θ r (r v) + sin θ rrt v = (cos θ,sin θ r) (,v) (cos θ, sin θ r), (18) which completes the proof! Any quaternion ˆQ with ˆQ ˆQ = 1 constitutes a rotation in SO(3), the components of the rotation matrix R are easily obtained from (18), ˆQ = (q,q 1,q,q 3 ), q + q 1 + q + q 3 = 1 R( ˆQ) 1 q q 3 (q 1q q q 3 ) (q 1 q 3 + q q ) = (q q 1 + q q 3 ) 1 q1 q 3 (q q 3 q q 1 ) (19) (q 3 q 1 q q ) (q 3 q + q q 1 ) 1 q1 q Back to our initial statement, that we can easily idenitify an isomorphic structure between quaternions and rotation matrices, R( ˆQ 1 ) R( ˆQ ) = R( ˆQ 1 ˆQ ). ().4 Quaternion differential algebra As in the case for the Rodrigues vector, we are interested in the differential structure of the rotation matrix with respect to the quaternion components. Since we have 4 quaternion components and only 3 rodrigues vector ones, we also have to address the problem of overparametrization. The quaternion ring is endowed with a commutative operation (addition) and a noncommutative multiplicative operation (composition), it is natural to consider the two differential structures. Additive differential rotation We can cast the rotation matrix R in a particular well suited quaternion form, essentially it s restating (18) R = Iq + qqt + q [q] + [q] [q], (1) I is the unit matrix. Using tensor notation in (1), I µν = δ µν, ( qq T ) µν = q µq ν, ([q] ) µν = ε µρν q ρ, ()
6 where δ µν is the Kroneckerdelta and ε µνρ the total antisymmetric tensor. Now we consider a differential rotation R( ˆQ + dˆq) R( ˆQ) and obtain R µν q = q δ µν + ε µρν q ρ, We used the following tensor contraction in (3) R µν q η = (δ ηµ q ν + q µ δ ην + q ε µην δ µν q η ). (3) ε αβγ ε αµν = δ βµ δ γν δ βν δ γµ. (4) Compounded differential rotation Alternatively, we can compose the differential rotation as the compound of the deviation from the unitquaternion Î (this represents the identity operation, nullrotation), by δ ˆQ = (Î + δˆq) ˆQ ˆQ = δˆq ˆQ, (Î + δˆq)(î + δˆq) = 1. (5) Then, we can write a differential rotation R((Î + δˆq) ˆQ) R( ˆQ) with the change of δˆq (5), [R( ˆQ + δ ˆQ) R( ˆQ)]v = δ ˆQ (,v) ˆQ + ˆQ (,v) δ ˆQ = δˆq (,v ) + (,v ) δˆq = δq v + δq v = δq R( ˆQ)v + δq (R( ˆQ)v), (6) hence the derivatives have a particularly simple form compared to (3), R µν δq = R µν, R µν δq α = ε αρµ R ρν. (7) Overparametrization, connection with the Rodrigues vector The rotation group SO(3) is a 3parameter group, that is reflected in the number of Eulerangles and the vector components of the Rodrigues vector. Quaternions have 4 real components, seemingly we have increased the number of parameters. However, for a quaternion ˆQ to represent a rotation it must lie on the normalizedsphere S 3, ˆQ ˆQ = 1. (8) This represents an additional constraint, that is not built into the quaternioncomponent derivatives (3,7).
7 We can choose to parameterize the quaternion with the Rodrigues vector (16), then the derivatives of a general rotation quaternion ˆq with respect to the independent rodrigues vector ω components are, ˆq ω α = ( r α sin θ, r α cos θ r + 1 θ sin θ (δ αβ r α r β )e β ). (9) Here we assume that < θ or equivalently < q. Equivalently we can express (9) in quaternion component form, q = 1 ω α q q η α, = 1 ω α (q sincx) q αq η q + sincx q δ αη, x = arctan q. (3) We notice, that (9,3) show that the quaternionconstraint manifests in nonlinear functions. On the other hand, if we were to parametrize the infinitesimal deviation from a unitquaternion, ˆq = Î + δˆq = (q,q) = (cos δθ,sin δθ r), r = δω/δθ, δθ = ω. (31) then the quaternionconstraint is reflected by a rather simple linear dependence, δq δq η δω =, α q= δω = 1 α q= δ αη. (3) Notice, that (3) is the limiting case of (3) for q. 3 Nonlinear function minimization over S 3 Given a bounded scalar function with SO(3) group parameters as arguments M f( ˆQ) = f(r( ˆQ)), ˆQ S 3, M IR (33) we are to find the (not necessarily unique) optimal quaternion ˆQ (f) which minimizes the scalar function f( ˆQ) in (33). In most cases analytical closed form solutions to (33) are not available. Numerical minimization algorithms utilize gradient information and at most deal with quadratic expansions of (33). Therefore it is natural to lay focus on functions of the form f = a + b µν R µν + c ηρ µν R µνr ηρ. (34) The arguments of f are implicitly subsumed in the rotation matrix components R µν. Equation (34) is frequently the result of a quasinewton approximation to (1). Despite of the seemingly simple structure of Eqn. (34)
8 with rotation matrix components occuring at most in order, hence we could call them R µν harmonic functions, it generally doesn t possess a closed form solution. Only for the particular case presented below the solution is known. 3.1 Pseudoquadratic functions The special case of finding a unit quaternion ˆQ min which minimizes f s (R( ˆQ)) N = a + Rc i d i (35) i=1 can be solved analytically [6, 7]. (35) represents the case where a cloud of N points c i in IR 3 are mapped by a rotation such that the result most closely resembles the cloud d i. We can write (35) in tensor notation, f s = a + d µ d µ d µ c ν R µν + c ν c ρ δ µη R µν R ηρ, (36) where we have omitted the sum over point index i, thus in terms of tensor coefficients (34) we have b µν = c µ d ν, Now, we use the orthogonality relation, c ηρ µν = c νc ρ δ µη. c ηρ µν R µνr ηρ = c ν c ρ δ νρ (37) and obtain an expression that is linear in the matrix elements R µν, hence (35) constitutes only a pseudoquadratic function, n f s = a + b µν R µν, b µν = d i c T i, a = a + c T i c i + d T i d i. (38) i=1 According to [7] we can find a solution to the R µν linear problem (38) by simply decomposing the general rank tensor b µν and restating the problem as in (35). Then we solve for ˆQ min by noticing that f s is quadratic in the quaternion components and therefore can be written as n f s = a + (q,q) Bi T B i(q,q) T, (39) i=1 with 4 4 matricies B i, [ ] (ci d B i = i ) T. d i c i [d i + c i ] A solution to (35) is given by the eigenvector ˆq of B T i B i with minimal eigenvalue.
9 4 Gradient search methods on the S 3 manifold In the absence of analytical solutions we resort to numerical minimization methods. To measure the minimization search performance of various methods we focus on the pseudoquadratic function f s and argue that the results are representative even in the general scenario of (34). 4.1 Iterative quadratic expansion in the Rodrigues vector ω or Eulerangles We start with a Rodrigues vector ω and expand the function f s to quadratic order in ω about ω. We keep the rotation matrix entries R µν to second order, (this basically means that we don t use the orthogonality relationship), f s ( ω) ã + (b µν + R µρ c ρ c ν ) R µν ω α } {{ } b T R µν R µρ ω α + ω α c ν c ρ ω β, ω α ω β } {{ } C (4) where ω = ω ω. Omitted are all contributions from R µν / ω α ω β. We need to calculate the exponential derivatives R µν ω α = R µν q,η q,η ω α, (41) which is exactly expression (1). Having obtained the minimal ω we then update the rodrigues vector ω ω 1 = ω + ω, and iteratively proceed with a local expansion (4) until convergence is reached. expansion with Eulerangles The same outline as depicted above applies to a local quadratic expansion with Eulerangles. Instead of the Rodrigues vector derivatives (41) we need to calculate the Eulerangle derivatives, R µν φ, R µν θ Then the update procedure follows the rule, and R µν ψ. (4) {φ,θ,ψ } {φ 1,θ 1,ψ 1 } = {φ + φ,θ + θ,ψ + ψ}.
10 4. Quaternionpath gradient search We construct a product sequence (path) from a suitable starting point ˆQ (), ˆQ (k) = (Î + ˆq(k) ) (Î + ˆq(k 1) )... ˆQ (), (43) with the constraint ˆQ (k) S 3 k, (44) meaning that ˆQ (k) at any step represents a rotation. NewtonRaphson, Lagrange multiplier technique One way to constrain quaternions on a unit sphere is by a Lagrange multiplier technique. As a test example we aim to obtain a quaternion sequence to find the minimum of the pseudoquadratic functional (35). As usual with the Lagrange multiplier technique, given f s and the normalization contraint for δˆq, we can deal with the function g g = f s + λ((1 + δq ) + δq 1). (45) Now, if we write derivatives of g with respect to the multiplicative differential quaternions, we obtain dg dδˆq = { bµν R µν + λ(1 + δq ) = ε αµρ b ρν R µν + λδq α =, α {1,,3} (46) Assuming R being constant for the gradient search, we find an easy solution to (46), i.e. starting with R( ˆQ () ), in the first step, then we can solve for the update ˆq, ˆq = Î + δˆq, λ = b µνr µν q, q α q = ε αµρ R µν b ρν R ηξ b ηξ. (47) Having found ˆq and properly normalized, we successively obtain a new starting point ˆQ (1) = ˆq ˆQ () with rotation matrix R( ˆQ (1) ) for the next search step and solve for another quaternion until convergence is reached. Experimental results follow. QuasiNewton method through infinitesimal exponential parametrization Instead of imposing the quaternion constraint by a Lagrange multiplier technique we could parametrize the deviation quaternion (Î + ˆq) in
11 (43) by its Rodriguesvector and assume that we are in the linear regime (3), f s ã + (b µν + R µρ c ρ c ν ) R µν R µν R µρ ω α + ω α c ν c ρ ω β, (48) δω } {{ α δω } α δω β } {{ } b T C where ã = a c T i c i. Using (7) and (3), R µν δω α = R µν δq,η δq,η δω α = (R µν δq δω α ε ηµρ R ρν δq η δω α ) = ε αµρ R ρν, (49) and we obtain the expression for b b α = ε αµρ (R ρν b µν R µξ c ξ R ρν c ν ). (5) To emphasize the structure of the symmetric matrix C we write, D αµ = R µν δω α c ν = ε αµρ R ρν c ν, C αβ = D αµ D T µβ. (51) We find ω in equation (48) is quickly solved using SVD, f s ω =, ω = 1 C 1 b, (5) and calculate the finite step update quaternion (Î + ˆq(k) ) using (31). With a new quaternion ˆQ (k+1) as obtained in (43), we calculate rotation matrices and derivatives thereof. This process is repeated until suffient convergence is reached. 5 Experimental results Here I present numerical experiments on the convergence of the various gradient minimization methods. We compare the standard methods as described in section 4.1 with the ones utilizing the quaternionpath methods, see section 4.. The iterative Eulerangle expansion and the iterative Rodrigues vector expansion are referred to as Standard 1 and Standard. The two methods utilizing quaternionpath gradients are referred to as Lagrange and Compounding. We initialize all experiments by a random selection of N points {c i } and choosing a random transformation R( ˆQ f ) to obtain a point cloud {d i } according to (35). Then we randomly initialize our first guess R( ˆQ () )
12 and employ the minimization methods. The graphs show the minimization trajectory in the Rodrigues vector space. The green dota represents the initial guess, the endpoints of the two red lines the two possible exact solutions. We track the convergence by counting the number of steps it takes to obtain the accuracy f s. This corresponds to the residual value of mismatching the euclidian distance (35). We clearly notice that in all scenarios the compounding quaternion derivative method is vastly superior and has superlinear convergence. Also it never failed in all tested cases. Most rarely does the Eulerangle method succeed. 6 Conclusions I have concisely introduced important rotation group SO(3) representations and presented their differential structure. I ve developed the new idea of a product (compounding) pathquaternions and demonstrated its advantages application towards numerical minimization methods. In the special pointmatching case this method is vastly superior than all its alternatives. It is expected that the superlinear convergence of the compounding pathquaternion approach is retained even in the case of more general function types that depend on SO(3) parameters.
13 6.1 N = data points NewtonRaphson 4 Lagrange w x w y 4  wx Compounding 43 derivatives wy w z wz   wy .5 Exponential 5 derivatives.5  wy Euler derivatives 5 wz  4 wz wx wx Accuracy Standard 1 Standard Lagrange Compounding
14 6. N = 5 data points Ω z Newton Raphson Lagrange Ω y 4 Ω z Ω x Compounding Ω 3derivatives y ΩxΩ Ω z Ω x Exponential Ω 3 derivatives y 4 Ω z Ω x Ω Euler derivatives y Accuracy Standard 1 Standard Lagrange Compounding
15 6.3 N = 1 data points w NewtonRaphson Lagrange x w y w Compounding derivatives x w y w z w z w Exponential derivatives x w y w y w Euler derivatives x w z w z   Accuracy Standard Standard Lagrange Compounding References 1. K. Kanatani, Analysis of 3D Rotation Fitting, Transactions of pattern analysis and machine intelligence, IEEE, Vol. 16, no. 5, S. L. Altman, Rotations, Quaternions and Double Groups. Clarendon Press, Oxford, 1986.
16 3. G. J. Minkler, Aerospace Coordinate Systems and Transformations. Magellan Book Company, Balitmore, MD, L. D. Landau and E. M. Lifshitz, Course of Theoretical Physiscs, Mechanics, ed. L.D. Landau, ButterworthHeinemann, 3rd edtion, X. Pennec and J. P. Thirion, A Framework for Uncertainty and Validation of 3D Registration Methods based on Points and Frames, International Journal of Computer Vision, 5(3), pp 39, B. K. P. Horn, Closed Form Solutions of Absolute Orientations Using Unit Quaternions, Journal of Optical Society of America, A4(4), pp 6964, J. Weng, T. S. Huang and N. Ahuja, Motion and Structure from Two Perspective Views: Algorithms, Error Analysis, and Error Estimation IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. II, No 5, pp , 1989.
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