borglab · varunagrawal · Jul 28, 2022 · Jun 14, 2022 · Jul 7, 2022 · Jul 8, 2022
diff --git a/gtsam/gtsam.i b/gtsam/gtsam.i
@@ -66,27 +66,6 @@ class KeySet {
   void serialize() const;
 };
 
-// Actually a vector<Key>
-class KeyVector {
-  KeyVector();
-  KeyVector(const gtsam::KeyVector& other);
-
-  // Note: no print function
-
-  // common STL methods
-  size_t size() const;
-  bool empty() const;
-  void clear();
-
-  // structure specific methods
-  size_t at(size_t i) const;
-  size_t front() const;
-  size_t back() const;
-  void push_back(size_t key) const;
-
-  void serialize() const;
-};
-
 // Actually a FastMap<Key,int>
 class KeyGroupMap {
   KeyGroupMap();

diff --git a/gtsam/nonlinear/CustomFactor.h b/gtsam/nonlinear/CustomFactor.h
@@ -19,8 +19,6 @@
 
 #include <gtsam/nonlinear/NonlinearFactor.h>
 
-using namespace gtsam;
-
 namespace gtsam {
 
 using JacobianVector = std::vector<Matrix>;

diff --git a/gtsam/nonlinear/GncParams.h b/gtsam/nonlinear/GncParams.h
@@ -72,8 +72,14 @@ class GTSAM_EXPORT GncParams {
   double relativeCostTol = 1e-5;  ///< If relative cost change is below this threshold, stop iterating
   double weightsTol = 1e-4;  ///< If the weights are within weightsTol from being binary, stop iterating (only for TLS)
   Verbosity verbosity = SILENT;  ///< Verbosity level
-  std::vector<size_t> knownInliers = std::vector<size_t>();  ///< Slots in the factor graph corresponding to measurements that we know are inliers
-  std::vector<size_t> knownOutliers = std::vector<size_t>();  ///< Slots in the factor graph corresponding to measurements that we know are outliers
+
+  //TODO(Varun) replace IndexVector with vector<size_t> once pybind11/stl.h is globally enabled.
+  /// Use IndexVector for inliers and outliers since it is fast + wrapping
+  using IndexVector = FastVector<uint64_t>;
+  ///< Slots in the factor graph corresponding to measurements that we know are inliers
+  IndexVector knownInliers = IndexVector();
+  ///< Slots in the factor graph corresponding to measurements that we know are outliers
+  IndexVector knownOutliers = IndexVector();
 
   /// Set the robust loss function to be used in GNC (chosen among the ones in GncLossType).
   void setLossType(const GncLossType type) {
@@ -114,7 +120,7 @@ class GTSAM_EXPORT GncParams {
    * This functionality is commonly used in SLAM when one may assume the odometry is outlier free, and
    * only apply GNC to prune outliers from the loop closures.
    * */
-  void setKnownInliers(const std::vector<size_t>& knownIn) {
+  void setKnownInliers(const IndexVector& knownIn) {
     for (size_t i = 0; i < knownIn.size(); i++){
       knownInliers.push_back(knownIn[i]);
     }
@@ -125,7 +131,7 @@ class GTSAM_EXPORT GncParams {
    * corresponds to the slots in the factor graph. For instance, if you have a nonlinear factor graph nfg,
    * and you provide  knownOut = {0, 2, 15}, GNC will not apply outlier rejection to nfg[0], nfg[2], and nfg[15].
    * */
-  void setKnownOutliers(const std::vector<size_t>& knownOut) {
+  void setKnownOutliers(const IndexVector& knownOut) {
     for (size_t i = 0; i < knownOut.size(); i++){
       knownOutliers.push_back(knownOut[i]);
     }
@@ -163,7 +169,7 @@ class GTSAM_EXPORT GncParams {
       std::cout << "knownInliers: " << knownInliers[i] << "\n";
     for (size_t i = 0; i < knownOutliers.size(); i++)
       std::cout << "knownOutliers: " << knownOutliers[i] << "\n";
-    baseOptimizerParams.print(str);
+    baseOptimizerParams.print("Base optimizer params: ");
   }
 };
 

diff --git a/gtsam/nonlinear/NonlinearFactor.cpp b/gtsam/nonlinear/NonlinearFactor.cpp
@@ -167,8 +167,9 @@ boost::shared_ptr<GaussianFactor> NoiseModelFactor::linearize(
     return GaussianFactor::shared_ptr(
         new JacobianFactor(terms, b,
             boost::static_pointer_cast<Constrained>(noiseModel_)->unit()));
-  else
+  else {
     return GaussianFactor::shared_ptr(new JacobianFactor(terms, b));
+  }
 }
 
 /* ************************************************************************* */

diff --git a/gtsam/nonlinear/custom.i b/gtsam/nonlinear/custom.i
@@ -0,0 +1,38 @@
+//*************************************************************************
+// Custom Factor wrapping
+//*************************************************************************
+
+namespace gtsam {
+
+#include <gtsam/nonlinear/CustomFactor.h>
+virtual class CustomFactor : gtsam::NoiseModelFactor {
+  /*
+   * Note CustomFactor will not be wrapped for MATLAB, as there is no supporting
+   * machinery there. This is achieved by adding `gtsam::CustomFactor` to the
+   * ignore list in `matlab/CMakeLists.txt`.
+   */
+  CustomFactor();
+  /*
+   * Example:
+   * ```
+   * def error_func(this: CustomFactor, v: gtsam.Values, H: List[np.ndarray]):
+   *    <calculated error>
+   *    if not H is None:
+   *        <calculate the Jacobian>
+   *        H[0] = J1 # 2-d numpy array for a Jacobian block
+   *        H[1] = J2
+   *        ...
+   *    return error # 1-d numpy array
+   *
+   * cf = CustomFactor(noise_model, keys, error_func)
+   * ```
+   */
+  CustomFactor(const gtsam::SharedNoiseModel& noiseModel,
+               const gtsam::KeyVector& keys,
+               const gtsam::CustomErrorFunction& errorFunction);
+
+  void print(string s = "",
+             gtsam::KeyFormatter keyFormatter = gtsam::DefaultKeyFormatter);
+};
+
+}
diff --git a/gtsam/nonlinear/nonlinear.i b/gtsam/nonlinear/nonlinear.i
@@ -99,11 +99,11 @@ class NonlinearFactorGraph {
   string dot(
       const gtsam::Values& values,
       const gtsam::KeyFormatter& keyFormatter = gtsam::DefaultKeyFormatter,
-      const GraphvizFormatting& writer = GraphvizFormatting());
+      const gtsam::GraphvizFormatting& writer = gtsam::GraphvizFormatting());
   void saveGraph(
       const string& s, const gtsam::Values& values,
       const gtsam::KeyFormatter& keyFormatter = gtsam::DefaultKeyFormatter,
-      const GraphvizFormatting& writer = GraphvizFormatting()) const;
+      const gtsam::GraphvizFormatting& writer = gtsam::GraphvizFormatting()) const;
 
   // enabling serialization functionality
   void serialize() const;
@@ -135,37 +135,6 @@ virtual class NoiseModelFactor : gtsam::NonlinearFactor {
   Vector whitenedError(const gtsam::Values& x) const;
 };
 
-#include <gtsam/nonlinear/CustomFactor.h>
-virtual class CustomFactor : gtsam::NoiseModelFactor {
-  /*
-   * Note CustomFactor will not be wrapped for MATLAB, as there is no supporting
-   * machinery there. This is achieved by adding `gtsam::CustomFactor` to the
-   * ignore list in `matlab/CMakeLists.txt`.
-   */
-  CustomFactor();
-  /*
-   * Example:
-   * ```
-   * def error_func(this: CustomFactor, v: gtsam.Values, H: List[np.ndarray]):
-   *    <calculated error>
-   *    if not H is None:
-   *        <calculate the Jacobian>
-   *        H[0] = J1 # 2-d numpy array for a Jacobian block
-   *        H[1] = J2
-   *        ...
-   *    return error # 1-d numpy array
-   *
-   * cf = CustomFactor(noise_model, keys, error_func)
-   * ```
-   */
-  CustomFactor(const gtsam::SharedNoiseModel& noiseModel,
-               const gtsam::KeyVector& keys,
-               const gtsam::CustomErrorFunction& errorFunction);
-
-  void print(string s = "",
-             gtsam::KeyFormatter keyFormatter = gtsam::DefaultKeyFormatter);
-};
-
 #include <gtsam/nonlinear/Values.h>
 class Values {
   Values();
@@ -544,12 +513,34 @@ virtual class DoglegParams : gtsam::NonlinearOptimizerParams {
 };
 
 #include <gtsam/nonlinear/GncParams.h>
+enum GncLossType {
+  GM /*Geman McClure*/,
+  TLS /*Truncated least squares*/
+};
+
 template<PARAMS>
 virtual class GncParams {
   GncParams(const PARAMS& baseOptimizerParams);
   GncParams();
-  void setVerbosityGNC(const This::Verbosity value);
-  void print(const string& str) const;
+  BaseOptimizerParameters baseOptimizerParams;
+  gtsam::GncLossType lossType;
+  size_t maxIterations;
+  double muStep;
+  double relativeCostTol;
+  double weightsTol;
+  Verbosity verbosity;
+  gtsam::KeyVector knownInliers;
+  gtsam::KeyVector knownOutliers;
+
+  void setLossType(const gtsam::GncLossType type);
+  void setMaxIterations(const size_t maxIter);
+  void setMuStep(const double step);
+  void setRelativeCostTol(double value);
+  void setWeightsTol(double value);
+  void setVerbosityGNC(const gtsam::This::Verbosity value);
+  void setKnownInliers(const gtsam::KeyVector& knownIn);
+  void setKnownOutliers(const gtsam::KeyVector& knownOut);
+  void print(const string& str = "GncParams: ") const;
 
   enum Verbosity {
     SILENT,
@@ -597,6 +588,11 @@ virtual class GncOptimizer {
   GncOptimizer(const gtsam::NonlinearFactorGraph& graph,
                const gtsam::Values& initialValues,
                const PARAMS& params);
+  void setInlierCostThresholds(const double inth);
+  const Vector& getInlierCostThresholds();
+  void setInlierCostThresholdsAtProbability(const double alpha);
+  void setWeights(const Vector w);
+  const Vector& getWeights();
   gtsam::Values optimize();
 };
 
@@ -873,7 +869,7 @@ template <T = {gtsam::Point2, gtsam::StereoPoint2, gtsam::Point3, gtsam::Rot2,
                gtsam::PinholeCamera<gtsam::Cal3Unified>,
                gtsam::imuBias::ConstantBias}>
 virtual class NonlinearEquality2 : gtsam::NoiseModelFactor {
-  NonlinearEquality2(Key key1, Key key2, double mu = 1e4);
+  NonlinearEquality2(gtsam::Key key1, gtsam::Key key2, double mu = 1e4);
   gtsam::Vector evaluateError(const T& x1, const T& x2);
 };
 

diff --git a/matlab/CMakeLists.txt b/matlab/CMakeLists.txt
@@ -73,6 +73,7 @@ set(interface_files
     ${GTSAM_SOURCE_DIR}/gtsam/geometry/geometry.i
     ${GTSAM_SOURCE_DIR}/gtsam/linear/linear.i
     ${GTSAM_SOURCE_DIR}/gtsam/nonlinear/nonlinear.i
+    ${GTSAM_SOURCE_DIR}/gtsam/nonlinear/custom.i
     ${GTSAM_SOURCE_DIR}/gtsam/symbolic/symbolic.i
     ${GTSAM_SOURCE_DIR}/gtsam/sam/sam.i
     ${GTSAM_SOURCE_DIR}/gtsam/slam/slam.i

diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt
@@ -61,6 +61,7 @@ set(interface_headers
     ${PROJECT_SOURCE_DIR}/gtsam/geometry/geometry.i
     ${PROJECT_SOURCE_DIR}/gtsam/linear/linear.i
     ${PROJECT_SOURCE_DIR}/gtsam/nonlinear/nonlinear.i
+    ${PROJECT_SOURCE_DIR}/gtsam/nonlinear/custom.i
     ${PROJECT_SOURCE_DIR}/gtsam/symbolic/symbolic.i
     ${PROJECT_SOURCE_DIR}/gtsam/sam/sam.i
     ${PROJECT_SOURCE_DIR}/gtsam/slam/slam.i

diff --git a/python/gtsam/preamble/custom.h b/python/gtsam/preamble/custom.h
@@ -0,0 +1,12 @@
+/* Please refer to:
+ * https://pybind11.readthedocs.io/en/stable/advanced/cast/stl.html
+ * These are required to save one copy operation on Python calls.
+ *
+ * NOTES
+ * =================
+ *
+ * `PYBIND11_MAKE_OPAQUE` will mark the type as "opaque" for the pybind11
+ * automatic STL binding, such that the raw objects can be accessed in Python.
+ * Without this they will be automatically converted to a Python object, and all
+ * mutations on Python side will not be reflected on C++.
+ */
diff --git a/python/gtsam/preamble/nonlinear.h b/python/gtsam/preamble/nonlinear.h
@@ -9,4 +9,4 @@
  * automatic STL binding, such that the raw objects can be accessed in Python.
  * Without this they will be automatically converted to a Python object, and all
  * mutations on Python side will not be reflected on C++.
- */
+ */
diff --git a/python/gtsam/specializations/custom.h b/python/gtsam/specializations/custom.h
@@ -0,0 +1,12 @@
+/* Please refer to:
+ * https://pybind11.readthedocs.io/en/stable/advanced/cast/stl.html
+ * These are required to save one copy operation on Python calls.
+ *
+ * NOTES
+ * =================
+ *
+ * `py::bind_vector` and similar machinery gives the std container a Python-like
+ * interface, but without the `<pybind11/stl.h>` copying mechanism. Combined
+ * with `PYBIND11_MAKE_OPAQUE` this allows the types to be modified with Python,
+ * and saves one copy operation.
+ */
diff --git a/python/gtsam/specializations/nonlinear.h b/python/gtsam/specializations/nonlinear.h
@@ -9,4 +9,4 @@
  * interface, but without the `<pybind11/stl.h>` copying mechanism. Combined
  * with `PYBIND11_MAKE_OPAQUE` this allows the types to be modified with Python,
  * and saves one copy operation.
- */
+ */