From 2ad0cd4417fac4669bae8710d81b38d6f5bea049 Mon Sep 17 00:00:00 2001
From: Richard Roberts <richard.jw.roberts@gmail.com>
Date: Thu, 3 Oct 2013 19:10:13 +0000
Subject: [PATCH] Fixed compile error on linux

---
 gtsam/linear/NoiseModel.cpp |  8 ++++----
 gtsam/linear/NoiseModel.h   | 12 ++++++------
 2 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/gtsam/linear/NoiseModel.cpp b/gtsam/linear/NoiseModel.cpp
index e10428034..56628c55d 100644
--- a/gtsam/linear/NoiseModel.cpp
+++ b/gtsam/linear/NoiseModel.cpp
@@ -107,7 +107,7 @@ void Gaussian::WhitenInPlace(Matrix& H) const {
 }
 
 /* ************************************************************************* */
-void Gaussian::WhitenInPlace(Eigen::Block<Matrix>& H) const {
+void Gaussian::WhitenInPlace(Eigen::Block<Matrix> H) const {
   H = thisR() * H;
 }
 
@@ -238,7 +238,7 @@ void Diagonal::WhitenInPlace(Matrix& H) const {
 }
 
 /* ************************************************************************* */
-void Diagonal::WhitenInPlace(Eigen::Block<Matrix>& H) const {
+void Diagonal::WhitenInPlace(Eigen::Block<Matrix> H) const {
   H = invsigmas().asDiagonal() * H;
 }
 
@@ -315,7 +315,7 @@ void Constrained::WhitenInPlace(Matrix& H) const {
 }
 
 /* ************************************************************************* */
-void Constrained::WhitenInPlace(Eigen::Block<Matrix>& H) const {
+void Constrained::WhitenInPlace(Eigen::Block<Matrix> H) const {
   // selective scaling
   // Scale row i of H by sigmas[i], basically multiplying H with diag(sigmas)
   // Set inf_mask flag is true so that if invsigmas[i] is inf, i.e. sigmas[i] = 0,
@@ -466,7 +466,7 @@ void Isotropic::WhitenInPlace(Matrix& H) const {
 }
 
 /* ************************************************************************* */
-void Isotropic::WhitenInPlace(Eigen::Block<Matrix>& H) const {
+void Isotropic::WhitenInPlace(Eigen::Block<Matrix> H) const {
   H *= invsigma_;
 }
 
diff --git a/gtsam/linear/NoiseModel.h b/gtsam/linear/NoiseModel.h
index 8f8fe835f..f73360579 100644
--- a/gtsam/linear/NoiseModel.h
+++ b/gtsam/linear/NoiseModel.h
@@ -199,7 +199,7 @@ namespace gtsam {
       /**
        * In-place version
        */
-      virtual void WhitenInPlace(Eigen::Block<Matrix>& H) const;
+      virtual void WhitenInPlace(Eigen::Block<Matrix> H) const;
 
       /**
        * Whiten a system, in place as well
@@ -297,7 +297,7 @@ namespace gtsam {
       virtual Vector unwhiten(const Vector& v) const;
       virtual Matrix Whiten(const Matrix& H) const;
       virtual void WhitenInPlace(Matrix& H) const;
-      virtual void WhitenInPlace(Eigen::Block<Matrix>& H) const;
+      virtual void WhitenInPlace(Eigen::Block<Matrix> H) const;
 
       /**
        * Return standard deviations (sqrt of diagonal)
@@ -447,7 +447,7 @@ namespace gtsam {
       /// with a non-zero sigma.  Other rows remain untouched.
       virtual Matrix Whiten(const Matrix& H) const;
       virtual void WhitenInPlace(Matrix& H) const;
-      virtual void WhitenInPlace(Eigen::Block<Matrix>& H) const;
+      virtual void WhitenInPlace(Eigen::Block<Matrix> H) const;
 
       /**
        * Apply QR factorization to the system [A b], taking into account constraints
@@ -527,7 +527,7 @@ namespace gtsam {
       virtual Vector unwhiten(const Vector& v) const;
       virtual Matrix Whiten(const Matrix& H) const;
       virtual void WhitenInPlace(Matrix& H) const;
-      virtual void WhitenInPlace(Eigen::Block<Matrix>& H) const;
+      virtual void WhitenInPlace(Eigen::Block<Matrix> H) const;
 
       /**
        * Return standard deviation
@@ -575,7 +575,7 @@ namespace gtsam {
       virtual Vector unwhiten(const Vector& v) const { return v; }
       virtual Matrix Whiten(const Matrix& H) const { return H; }
       virtual void WhitenInPlace(Matrix& H) const {}
-      virtual void WhitenInPlace(Eigen::Block<Matrix>& H) const {}
+      virtual void WhitenInPlace(Eigen::Block<Matrix> H) const {}
 
     private:
       /** Serialization function */
@@ -710,7 +710,7 @@ namespace gtsam {
       };
 
       /// Cauchy implements the "Cauchy" robust error model (Lee2013IROS).  Contributed by:
-      ///   Dipl.-Inform. Jan Oberländer (M.Sc.), FZI Research Center for
+      ///   Dipl.-Inform. Jan Oberl�nder (M.Sc.), FZI Research Center for
       ///   Information Technology, Karlsruhe, Germany.
       ///   oberlaender@fzi.de
       /// Thanks Jan!