makeNoiseModelRobust assumes responsibility for robustifying noise models

gtsam/sfm/ShonanAveraging.h

@@ -83,13 +83,13 @@ struct GTSAM_EXPORT ShonanAveragingParameters {
   void setUseHuber(bool value) { useHuber = value; }
   bool getUseHuber() { return useHuber; }
 
+  /// Print the parameters and flags used for rotation averaging.
   void print() const {
     std::cout << " ShonanAveragingParameters: " << std::endl;
-    std::cout << " alpha: " << alpha  << std::endl;
+    std::cout << " alpha: " << alpha << std::endl;
-    std::cout << " beta: " << beta  << std::endl;
+    std::cout << " beta: " << beta << std::endl;
-    std::cout << " gamma: " << gamma  << std::endl;
+    std::cout << " gamma: " << gamma << std::endl;
-    std::cout << " useHuber: " << useHuber  << std::endl;
+    std::cout << " useHuber: " << useHuber << std::endl;
-    std::cout << " --------------------------" << std::endl;
   }
 };
 
@@ -164,11 +164,33 @@ class GTSAM_EXPORT ShonanAveraging {
     return measurements_[k];
   }
 
-  /// wrap factors with robust Huber loss
+  /**
-  Measurements makeNoiseModelRobust(const Measurements& measurements) const {
+   * Update factors to use robust Huber loss.
-    Measurements robustMeasurements = measurements;
+   * 
-    for (auto &measurement : robustMeasurements) {
+   * @param measurements Vector of BinaryMeasurements.
-      measurement = BinaryMeasurement<Rot>(measurement, true);
+   * @param k Huber noise model threshold.
+   */
+  Measurements makeNoiseModelRobust(const Measurements &measurements,
+                                    double k = 1.345) const {
+    Measurements robustMeasurements;
+    for (auto &measurement : measurements) {
+
+      auto model = measurement.noiseModel();
+      const auto &robust =
+          boost::dynamic_pointer_cast<noiseModel::Robust>(model);
+
+      SharedNoiseModel robust_model;
+      // Check if the noise model is already robust
+      if (robust) {
+        robust_model = model;
+      } else {
+        // make robust
+        robust_model = noiseModel::Robust::Create(
+            noiseModel::mEstimator::Huber::Create(k), model);
+      }
+      BinaryMeasurement<Rot> meas(measurement.key1(), measurement.key2(),
+                                      measurement.measured(), robust_model);
+      robustMeasurements.push_back(meas);
     }
     return robustMeasurements;
   }