don't optimize each time
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				|  | @ -44,7 +44,7 @@ using symbol_shorthand::M; | ||||||
| using symbol_shorthand::X; | using symbol_shorthand::X; | ||||||
| 
 | 
 | ||||||
| // Testing params
 | // Testing params
 | ||||||
| const size_t max_loop_count = 3000;  // 2000;  // 200 //2000 //8000
 | const size_t max_loop_count = 2000;  // 2000;  // 200 //2000 //8000
 | ||||||
| 
 | 
 | ||||||
| noiseModel::Diagonal::shared_ptr prior_noise_model = | noiseModel::Diagonal::shared_ptr prior_noise_model = | ||||||
|     noiseModel::Diagonal::Sigmas( |     noiseModel::Diagonal::Sigmas( | ||||||
|  | @ -122,8 +122,8 @@ void SmootherUpdate(HybridSmoother& smoother, HybridNonlinearFactorGraph& graph, | ||||||
|   // std::cout << "index: " << index << std::endl;
 |   // std::cout << "index: " << index << std::endl;
 | ||||||
|   smoother.update(linearized, maxNrHypotheses); |   smoother.update(linearized, maxNrHypotheses); | ||||||
|   graph.resize(0); |   graph.resize(0); | ||||||
|   HybridValues delta = smoother.hybridBayesNet().optimize(); |   // HybridValues delta = smoother.hybridBayesNet().optimize();
 | ||||||
|   result->insert_or_assign(initial.retract(delta.continuous())); |   // result->insert_or_assign(initial.retract(delta.continuous()));
 | ||||||
| } | } | ||||||
| 
 | 
 | ||||||
| /* ************************************************************************* */ | /* ************************************************************************* */ | ||||||
|  |  | ||||||
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