27 #ifndef __se3_symmetric3_hpp__ 28 #define __se3_symmetric3_hpp__ 30 #include "pinocchio/macros.hpp" 35 template<
typename _Scalar,
int _Options>
39 typedef _Scalar Scalar;
40 enum { Options = _Options };
41 typedef Eigen::Matrix<Scalar,3,1,Options> Vector3;
42 typedef Eigen::Matrix<Scalar,6,1,Options> Vector6;
43 typedef Eigen::Matrix<Scalar,3,3,Options> Matrix3;
44 typedef Eigen::Matrix<Scalar,2,2,Options> Matrix2;
45 typedef Eigen::Matrix<Scalar,3,2,Options> Matrix32;
47 EIGEN_MAKE_ALIGNED_OPERATOR_NEW
50 Symmetric3Tpl(): data_() {}
61 template<
typename Sc,
int N,
int Opt>
62 explicit Symmetric3Tpl(
const Eigen::Matrix<Sc,N,N,Opt> & I)
64 EIGEN_STATIC_ASSERT(N==3,THIS_METHOD_IS_ONLY_FOR_MATRICES_OF_A_SPECIFIC_SIZE)
65 assert( (I-I.transpose()).isMuchSmallerThan(I) );
67 data_(1) = I(1,0); data_(2) = I(1,1);
68 data_(3) = I(2,0); data_(4) = I(2,1); data_(5) = I(2,2);
71 explicit Symmetric3Tpl(const Vector6 & I) : data_(I) {}
73 Symmetric3Tpl(
const Scalar & a0,
const Scalar & a1,
const Scalar & a2,
74 const Scalar & a3,
const Scalar & a4,
const Scalar & a5)
75 { data_ << a0,a1,a2,a3,a4,a5; }
77 static Symmetric3Tpl Zero() {
return Symmetric3Tpl(Vector6::Zero()); }
78 void setZero() { data_.setZero(); }
80 static Symmetric3Tpl Random() {
return RandomPositive(); }
84 a = Scalar(std::rand())/RAND_MAX*2.0-1.0,
85 b = Scalar(std::rand())/RAND_MAX*2.0-1.0,
86 c = Scalar(std::rand())/RAND_MAX*2.0-1.0,
87 d = Scalar(std::rand())/RAND_MAX*2.0-1.0,
88 e = Scalar(std::rand())/RAND_MAX*2.0-1.0,
89 f = Scalar(std::rand())/RAND_MAX*2.0-1.0;
91 data_ << a, b, c, d, e, f;
94 static Symmetric3Tpl Identity() {
return Symmetric3Tpl(1, 0, 1, 0, 0, 1); }
95 void setIdentity() { data_ << 1, 0, 1, 0, 0, 1; }
98 bool operator== (
const Symmetric3Tpl & S2)
const {
return data_ == S2.data_; }
100 bool isApprox(
const Symmetric3Tpl & other,
101 const Scalar & prec = Eigen::NumTraits<Scalar>::dummy_precision())
const 102 {
return data_.isApprox(other.data_,prec); }
104 void fill(
const Scalar value) { data_.fill(value); }
112 const Scalar & x = v[0], & y = v[1], & z = v[2];
115 x*z , y*z , -x*x-y*y );
121 const Scalar & x = v.v[0], & y = v.v[1], & z = v.v[2];
123 data_[1]-x*y,data_[2]+x*x+z*z,
124 data_[3]-x*z,data_[4]-y*z,data_[5]+x*x+y*y);
129 const Scalar & x = v.v[0], & y = v.v[1], & z = v.v[2];
131 data_[1]-=x*y; data_[2]+=x*x+z*z;
132 data_[3]-=x*z; data_[4]-=y*z; data_[5]+=x*x+y*y;
137 friend Matrix3 operator- (
const Symmetric3Tpl & S,
const Eigen::MatrixBase <D> & M)
139 EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(D,3,3);
140 Matrix3 result (S.matrix());
154 const Scalar & x = v[0], & y = v[1], & z = v[2];
157 m* x*z,m* y*z,-m*(x*x+y*y));
166 const Scalar & x = v.v[0], & y = v.v[1], & z = v.v[2];
168 data_[1]-v.m* x*y, data_[2]+v.m*(x*x+z*z),
169 data_[3]-v.m* x*z, data_[4]-v.m* y*z,
170 data_[5]+v.m*(x*x+y*y));
175 const Scalar & x = v.v[0], & y = v.v[1], & z = v.v[2];
176 data_[0]+=v.m*(y*y+z*z);
177 data_[1]-=v.m* x*y; data_[2]+=v.m*(x*x+z*z);
178 data_[3]-=v.m* x*z; data_[4]-=v.m* y*z; data_[5]+=v.m*(x*x+y*y);
182 const Vector6 & data ()
const {
return data_;}
183 Vector6 & data () {
return data_;}
197 a = Scalar(std::rand())/RAND_MAX*2.0-1.0,
198 b = Scalar(std::rand())/RAND_MAX*2.0-1.0,
199 c = Scalar(std::rand())/RAND_MAX*2.0-1.0,
200 d = Scalar(std::rand())/RAND_MAX*2.0-1.0,
201 e = Scalar(std::rand())/RAND_MAX*2.0-1.0,
202 f = Scalar(std::rand())/RAND_MAX*2.0-1.0;
204 a*b+b*c+d*e, b*b+c*c+e*e,
205 a*d+b*e+d*f, b*d+c*e+e*f, d*d+e*e+f*f );
208 Matrix3 matrix()
const 211 res(0,0) = data_(0); res(0,1) = data_(1); res(0,2) = data_(3);
212 res(1,0) = data_(1); res(1,1) = data_(2); res(1,2) = data_(4);
213 res(2,0) = data_(3); res(2,1) = data_(4); res(2,2) = data_(5);
216 operator Matrix3 ()
const {
return matrix(); }
218 Scalar vtiv (
const Vector3 & v)
const 220 const Scalar & x = v[0];
221 const Scalar & y = v[1];
222 const Scalar & z = v[2];
224 const Scalar xx = x*x;
225 const Scalar xy = x*y;
226 const Scalar xz = x*z;
227 const Scalar yy = y*y;
228 const Scalar yz = y*z;
229 const Scalar zz = z*z;
231 return data_(0)*xx + data_(2)*yy + data_(5)*zz + 2.*(data_(1)*xy + data_(3)*xz + data_(4)*yz);
241 data_ += s2.data_;
return *
this;
244 Vector3 operator*(
const Vector3 &v)
const 247 data_(0) * v(0) + data_(1) * v(1) + data_(3) * v(2),
248 data_(1) * v(0) + data_(2) * v(1) + data_(4) * v(2),
249 data_(3) * v(0) + data_(4) * v(1) + data_(5) * v(2)
265 const Scalar& operator()(
const int &i,
const int &j)
const 267 return ((i!=2)&&(j!=2)) ? data_[i+j] : data_[i+j+1];
272 assert( (S-S.transpose()).isMuchSmallerThan(S) );
274 data_(1)-S(1,0), data_(2)-S(1,1),
275 data_(3)-S(2,0), data_(4)-S(2,1), data_(5)-S(2,2) );
280 assert( (S-S.transpose()).isMuchSmallerThan(S) );
282 data_(1)+S(1,0), data_(2)+S(1,1),
283 data_(3)+S(2,0), data_(4)+S(2,1), data_(5)+S(2,2) );
295 data_(0) - data_(5), data_(1),
296 data_(1), data_(2) - data_(5),
297 2*data_(3), data_(4) + data_(4);
305 EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(D,3,3);
306 assert( (R.transpose()*R).isApprox(Matrix3::Identity()) );
314 const Matrix2 Y( R.template block<2,3>(1,0) * L );
317 Sres.data_(1) = Y(0,0)*R(0,0) + Y(0,1)*R(0,1);
318 Sres.data_(2) = Y(0,0)*R(1,0) + Y(0,1)*R(1,1);
319 Sres.data_(3) = Y(1,0)*R(0,0) + Y(1,1)*R(0,1);
320 Sres.data_(4) = Y(1,0)*R(1,0) + Y(1,1)*R(1,1);
321 Sres.data_(5) = Y(1,0)*R(2,0) + Y(1,1)*R(2,1);
324 const Vector3 r(-R(0,0)*data_(4) + R(0,1)*data_(3),
325 -R(1,0)*data_(4) + R(1,1)*data_(3),
326 -R(2,0)*data_(4) + R(2,1)*data_(3));
329 Sres.data_(0) = L(0,0) + L(1,1) - Sres.data_(2) - Sres.data_(5);
332 Sres.data_(0) += data_(5);
333 Sres.data_(1) += r(2); Sres.data_(2)+= data_(5);
334 Sres.data_(3) +=-r(1); Sres.data_(4)+= r(0); Sres.data_(5) += data_(5);
346 #endif // ifndef __se3_symmetric3_hpp__
Matrix32 decomposeltI() const
Computes L for a symmetric matrix A.