Rigid transformation

A rigid transformation is formally defined as a transformation that, when acting on any vector v, produces a transformed vector T(v) of the form

where R^T = R⁻¹ (i.e., R is an orthogonal transformation), and t is a vector giving the translation of the origin.

A proper rigid transformation has, in addition,

which means that R does not produce a reflection, and hence it represents a rotation (an orientation-preserving orthogonal transformation). Indeed, when an orthogonal transformation matrix produces a reflection, its determinant is −1.

A measure of distance between points, or metric, is needed in order to confirm that a transformation is rigid. The Euclidean distance formula for Rⁿ is the generalization of the Pythagorean theorem. The formula gives the distance squared between two points X and Y as the sum of the squares of the distances along the coordinate axes, that is $${\displaystyle d\left(\mathbf {X} ,\mathbf {Y} \right)^{2}=\left(X_{1}-Y_{1}\right)^{2}+\left(X_{2}-Y_{2}\right)^{2}+\dots +\left(X_{n}-Y_{n}\right)^{2}=\left(\mathbf {X} -\mathbf {Y} \right)\cdot \left(\mathbf {X} -\mathbf {Y} \right).}$$  where X = (X₁, X₂, ..., X_n) and Y = (Y₁, Y₂, ..., Y_n), and the dot denotes the scalar product.

Using this distance formula, a rigid transformation g : Rⁿ → Rⁿ has the property, $${\displaystyle d(g(\mathbf {X} ),g(\mathbf {Y} ))^{2}=d(\mathbf {X} ,\mathbf {Y} )^{2}.}$$ 

A translation of a vector space adds a vector d to every vector in the space, which means it is the transformation

It is easy to show that this is a rigid transformation by showing that the distance between translated vectors equal the distance between the original vectors: $${\displaystyle d(\mathbf {v} +\mathbf {d} ,\mathbf {w} +\mathbf {d} )^{2}=(\mathbf {v} +\mathbf {d} -\mathbf {w} -\mathbf {d} )\cdot (\mathbf {v} +\mathbf {d} -\mathbf {w} -\mathbf {d} )=(\mathbf {v} -\mathbf {w} )\cdot (\mathbf {v} -\mathbf {w} )=d(\mathbf {v} ,\mathbf {w} )^{2}.}$$ 

A linear transformation of a vector space, L : Rⁿ → Rⁿ, preserves linear combinations, $${\displaystyle L(\mathbf {V} )=L(a\mathbf {v} +b\mathbf {w} )=aL(\mathbf {v} )+bL(\mathbf {w} ).}$$  A linear transformation L can be represented by a matrix, which means

where [L] is an n×n matrix.

A linear transformation is a rigid transformation if it satisfies the condition, $${\displaystyle d([L]\mathbf {v} ,[L]\mathbf {w} )^{2}=d(\mathbf {v} ,\mathbf {w} )^{2},}$$  that is $${\displaystyle d([L]\mathbf {v} ,[L]\mathbf {w} )^{2}=([L]\mathbf {v} -[L]\mathbf {w} )\cdot ([L]\mathbf {v} -[L]\mathbf {w} )=([L](\mathbf {v} -\mathbf {w} ))\cdot ([L](\mathbf {v} -\mathbf {w} )).}$$  Now use the fact that the scalar product of two vectors v.w can be written as the matrix operation v^Tw, where the T denotes the matrix transpose, we have $${\displaystyle d([L]\mathbf {v} ,[L]\mathbf {w} )^{2}=(\mathbf {v} -\mathbf {w} )^{\mathsf {T}}[L]^{\mathsf {T}}[L](\mathbf {v} -\mathbf {w} ).}$$  Thus, the linear transformation L is rigid if its matrix satisfies the condition $${\displaystyle [L]^{\mathsf {T}}[L]=[I],}$$  where [I] is the identity matrix. Matrices that satisfy this condition are called orthogonal matrices. This condition actually requires the columns of these matrices to be orthogonal unit vectors.

Matrices that satisfy this condition form a mathematical group under the operation of matrix multiplication called the orthogonal group of n×n matrices and denoted O(n).

Compute the determinant of the condition for an orthogonal matrix to obtain $${\displaystyle \det \left([L]^{\mathsf {T}}[L]\right)=\det[L]^{2}=\det[I]=1,}$$  which shows that the matrix [L] can have a determinant of either +1 or −1. Orthogonal matrices with determinant −1 are reflections, and those with determinant +1 are rotations. Notice that the set of orthogonal matrices can be viewed as consisting of two manifolds in R^n×n separated by the set of singular matrices.

The set of rotation matrices is called the special orthogonal group, and denoted SO(n). It is an example of a Lie group because it has the structure of a manifold.

• Deformation (mechanics)
• Motion (geometry)
• Rigid body dynamics