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fixed typo and warning (int VS size_t)

release/4.3a0
Luca 2015-03-27 18:39:37 -04:00
parent 32c6453ee6
commit cdde347350
2 changed files with 4 additions and 4 deletions
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4
gtsam/geometry/CameraSet.h
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@ -93,7 +93,7 @@ public:
} }
/** /**
* Project a point (posibly Unit3 at infinity), with derivatives * Project a point (possibly Unit3 at infinity), with derivatives
* Note that F is a sparse block-diagonal matrix, so instead of a large dense * Note that F is a sparse block-diagonal matrix, so instead of a large dense
* matrix this function returns the diagonal blocks. * matrix this function returns the diagonal blocks.
* throws CheiralityException * throws CheiralityException
@ -148,7 +148,7 @@ public:
const Matrix& E, const Eigen::Matrix<double, N, N>& P, const Vector& b) { const Matrix& E, const Eigen::Matrix<double, N, N>& P, const Vector& b) {
// a single point is observed in m cameras // a single point is observed in m cameras
int m = Fs.size(); size_t m = Fs.size();
// Create a SymmetricBlockMatrix // Create a SymmetricBlockMatrix
size_t M1 = D * m + 1; size_t M1 = D * m + 1;

4
gtsam/slam/RegularImplicitSchurFactor.h
View File

@ -152,7 +152,7 @@ public:
* E_.block<ZDim, 3>(ZDim * k, 0); * E_.block<ZDim, 3>(ZDim * k, 0);
Eigen::Matrix<double, D, 1> dj; Eigen::Matrix<double, D, 1> dj;
for (size_t k = 0; k < D; ++k) { // for each diagonal element of the camera hessian for (int k = 0; k < D; ++k) { // for each diagonal element of the camera hessian
// Vector column_k_Fj = Fj.col(k); // Vector column_k_Fj = Fj.col(k);
dj(k) = Fj.col(k).squaredNorm(); // dot(column_k_Fj, column_k_Fj); dj(k) = Fj.col(k).squaredNorm(); // dot(column_k_Fj, column_k_Fj);
// Vector column_k_FtE = FtE.row(k); // Vector column_k_FtE = FtE.row(k);
@ -184,7 +184,7 @@ public:
* E_.block<ZDim, 3>(ZDim * pos, 0); * E_.block<ZDim, 3>(ZDim * pos, 0);
DVector dj; DVector dj;
for (size_t k = 0; k < D; ++k) { // for each diagonal element of the camera hessian for (int k = 0; k < D; ++k) { // for each diagonal element of the camera hessian
dj(k) = Fj.col(k).squaredNorm(); dj(k) = Fj.col(k).squaredNorm();
// (1 x 1) = (1 x 3) * (3 * 3) * (3 x 1) // (1 x 1) = (1 x 3) * (3 * 3) * (3 x 1)
dj(k) -= FtE.row(k) * PointCovariance_ * FtE.row(k).transpose(); dj(k) -= FtE.row(k) * PointCovariance_ * FtE.row(k).transpose();