renamed enum
lcarlone
parent
06dfeb7ac5
commit
eea52766d1
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@ -44,7 +44,7 @@ public:
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};
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/** Choice of robust loss function for GNC */
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enum RobustLossType {
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enum GncLossType {
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GM /*Geman McClure*/, TLS /*Truncated least squares*/
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};
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@ -61,7 +61,7 @@ public:
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/// GNC parameters
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BaseOptimizerParameters baseOptimizerParams; /*optimization parameters used to solve the weighted least squares problem at each GNC iteration*/
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/// any other specific GNC parameters:
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RobustLossType lossType = TLS; /* default loss*/
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GncLossType lossType = TLS; /* default loss*/
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size_t maxIterations = 100; /* maximum number of iterations*/
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double barcSq = 1.0; /* a factor is considered an inlier if factor.error() < barcSq. Note that factor.error() whitens by the covariance*/
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double muStep = 1.4; /* multiplicative factor to reduce/increase the mu in gnc */
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@ -70,8 +70,8 @@ public:
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Verbosity verbosity = SILENT; /* verbosity level */
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std::vector<size_t> knownInliers = std::vector<size_t>(); /* slots in the factor graph corresponding to measurements that we know are inliers */
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/// Set the robust loss function to be used in GNC (chosen among the ones in RobustLossType)
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void setLossType(const RobustLossType type) {
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/// Set the robust loss function to be used in GNC (chosen among the ones in GncLossType)
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void setLossType(const GncLossType type) {
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lossType = type;
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}
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/// Set the maximum number of iterations in GNC (changing the max nr of iters might lead to less accurate solutions and is not recommended)
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@ -101,8 +101,8 @@ public:
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void setWeightsTol(double value) { weightsTol = value;
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}
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/// Set the verbosity level
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void setVerbosityGNC(const Verbosity verbosity) {
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verbosity = verbosity;
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void setVerbosityGNC(const Verbosity value) {
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verbosity = value;
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}
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/** (Optional) Provide a vector of measurements that must be considered inliers. The enties in the vector
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* corresponds to the slots in the factor graph. For instance, if you have a nonlinear factor graph nfg,
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@ -66,7 +66,7 @@ TEST(GncOptimizer, gncParamsConstructor) {
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// change something at the gncParams level
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GncParams<GaussNewtonParams> gncParams2c(gncParams2b);
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gncParams2c.setLossType(GncParams<GaussNewtonParams>::RobustLossType::GM);
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gncParams2c.setLossType(GncParams<GaussNewtonParams>::GncLossType::GM);
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CHECK(!gncParams2c.equals(gncParams2b.baseOptimizerParams));
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}
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@ -119,7 +119,7 @@ TEST(GncOptimizer, initializeMu) {
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// testing GM mu initialization
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GncParams<LevenbergMarquardtParams> gncParams;
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gncParams.setLossType(
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GncParams<LevenbergMarquardtParams>::RobustLossType::GM);
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GncParams<LevenbergMarquardtParams>::GncLossType::GM);
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auto gnc_gm =
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GncOptimizer<GncParams<LevenbergMarquardtParams>>(fg, initial, gncParams);
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// according to rmk 5 in the gnc paper: m0 = 2 rmax^2 / barcSq
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@ -128,7 +128,7 @@ TEST(GncOptimizer, initializeMu) {
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// testing TLS mu initialization
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gncParams.setLossType(
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GncParams<LevenbergMarquardtParams>::RobustLossType::TLS);
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GncParams<LevenbergMarquardtParams>::GncLossType::TLS);
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auto gnc_tls =
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GncOptimizer<GncParams<LevenbergMarquardtParams>>(fg, initial, gncParams);
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// according to rmk 5 in the gnc paper: m0 = barcSq / (2 * rmax^2 - barcSq)
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@ -147,7 +147,7 @@ TEST(GncOptimizer, updateMuGM) {
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GncParams<LevenbergMarquardtParams> gncParams;
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gncParams.setLossType(
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GncParams<LevenbergMarquardtParams>::RobustLossType::GM);
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GncParams<LevenbergMarquardtParams>::GncLossType::GM);
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gncParams.setMuStep(1.4);
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auto gnc =
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GncOptimizer<GncParams<LevenbergMarquardtParams>>(fg, initial, gncParams);
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@ -172,7 +172,7 @@ TEST(GncOptimizer, updateMuTLS) {
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GncParams<LevenbergMarquardtParams> gncParams;
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gncParams.setMuStep(1.4);
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gncParams.setLossType(
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GncParams<LevenbergMarquardtParams>::RobustLossType::TLS);
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GncParams<LevenbergMarquardtParams>::GncLossType::TLS);
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auto gnc =
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GncOptimizer<GncParams<LevenbergMarquardtParams>>(fg, initial, gncParams);
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@ -192,7 +192,7 @@ TEST(GncOptimizer, checkMuConvergence) {
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{
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GncParams<LevenbergMarquardtParams> gncParams;
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gncParams.setLossType(
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GncParams<LevenbergMarquardtParams>::RobustLossType::GM);
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GncParams<LevenbergMarquardtParams>::GncLossType::GM);
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auto gnc =
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GncOptimizer<GncParams<LevenbergMarquardtParams>>(fg, initial, gncParams);
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@ -202,7 +202,7 @@ TEST(GncOptimizer, checkMuConvergence) {
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{
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GncParams<LevenbergMarquardtParams> gncParams;
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gncParams.setLossType(
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GncParams<LevenbergMarquardtParams>::RobustLossType::TLS);
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GncParams<LevenbergMarquardtParams>::GncLossType::TLS);
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auto gnc =
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GncOptimizer<GncParams<LevenbergMarquardtParams>>(fg, initial, gncParams);
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@ -256,7 +256,7 @@ TEST(GncOptimizer, checkWeightsConvergence) {
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{
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GncParams<LevenbergMarquardtParams> gncParams;
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gncParams.setLossType(
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GncParams<LevenbergMarquardtParams>::RobustLossType::GM);
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GncParams<LevenbergMarquardtParams>::GncLossType::GM);
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auto gnc =
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GncOptimizer<GncParams<LevenbergMarquardtParams>>(fg, initial, gncParams);
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@ -266,7 +266,7 @@ TEST(GncOptimizer, checkWeightsConvergence) {
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{
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GncParams<LevenbergMarquardtParams> gncParams;
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gncParams.setLossType(
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GncParams<LevenbergMarquardtParams>::RobustLossType::TLS);
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GncParams<LevenbergMarquardtParams>::GncLossType::TLS);
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auto gnc =
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GncOptimizer<GncParams<LevenbergMarquardtParams>>(fg, initial, gncParams);
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@ -277,7 +277,7 @@ TEST(GncOptimizer, checkWeightsConvergence) {
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{
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GncParams<LevenbergMarquardtParams> gncParams;
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gncParams.setLossType(
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GncParams<LevenbergMarquardtParams>::RobustLossType::TLS);
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GncParams<LevenbergMarquardtParams>::GncLossType::TLS);
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auto gnc =
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GncOptimizer<GncParams<LevenbergMarquardtParams>>(fg, initial, gncParams);
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@ -288,7 +288,7 @@ TEST(GncOptimizer, checkWeightsConvergence) {
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{
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GncParams<LevenbergMarquardtParams> gncParams;
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gncParams.setLossType(
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GncParams<LevenbergMarquardtParams>::RobustLossType::TLS);
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GncParams<LevenbergMarquardtParams>::GncLossType::TLS);
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gncParams.setWeightsTol(0.1);
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auto gnc =
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GncOptimizer<GncParams<LevenbergMarquardtParams>>(fg, initial, gncParams);
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@ -311,7 +311,7 @@ TEST(GncOptimizer, checkConvergenceTLS) {
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GncParams<LevenbergMarquardtParams> gncParams;
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gncParams.setRelativeCostTol(1e-5);
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gncParams.setLossType(
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GncParams<LevenbergMarquardtParams>::RobustLossType::TLS);
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GncParams<LevenbergMarquardtParams>::GncLossType::TLS);
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auto gnc =
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GncOptimizer<GncParams<LevenbergMarquardtParams>>(fg, initial, gncParams);
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@ -338,7 +338,7 @@ TEST(GncOptimizer, calculateWeightsGM) {
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GaussNewtonParams gnParams;
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GncParams<GaussNewtonParams> gncParams(gnParams);
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gncParams.setLossType(
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GncParams<GaussNewtonParams>::RobustLossType::GM);
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GncParams<GaussNewtonParams>::GncLossType::GM);
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auto gnc = GncOptimizer<GncParams<GaussNewtonParams>>(fg, initial, gncParams);
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double mu = 1.0;
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Vector weights_actual = gnc.calculateWeights(initial, mu);
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@ -373,7 +373,7 @@ TEST(GncOptimizer, calculateWeightsTLS) {
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GaussNewtonParams gnParams;
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GncParams<GaussNewtonParams> gncParams(gnParams);
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gncParams.setLossType(
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GncParams<GaussNewtonParams>::RobustLossType::TLS);
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GncParams<GaussNewtonParams>::GncLossType::TLS);
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auto gnc = GncOptimizer<GncParams<GaussNewtonParams>>(fg, initial, gncParams);
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double mu = 1.0;
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Vector weights_actual = gnc.calculateWeights(initial, mu);
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@ -408,7 +408,7 @@ TEST(GncOptimizer, calculateWeightsTLS2) {
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GaussNewtonParams gnParams;
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GncParams<GaussNewtonParams> gncParams(gnParams);
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gncParams.setLossType(
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GncParams<GaussNewtonParams>::RobustLossType::TLS);
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GncParams<GaussNewtonParams>::GncLossType::TLS);
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gncParams.setInlierCostThreshold(0.51); // if inlier threshold is slightly larger than 0.5, then measurement is inlier
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auto gnc = GncOptimizer<GncParams<GaussNewtonParams>>(nfg, initial, gncParams);
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double mu = 1e6;
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@ -424,7 +424,7 @@ TEST(GncOptimizer, calculateWeightsTLS2) {
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GaussNewtonParams gnParams;
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GncParams<GaussNewtonParams> gncParams(gnParams);
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gncParams.setLossType(
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GncParams<GaussNewtonParams>::RobustLossType::TLS);
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GncParams<GaussNewtonParams>::GncLossType::TLS);
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gncParams.setInlierCostThreshold(0.49); // if inlier threshold is slightly below 0.5, then measurement is outlier
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auto gnc = GncOptimizer<GncParams<GaussNewtonParams>>(nfg, initial, gncParams);
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double mu = 1e6; // very large mu recovers original TLS cost
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@ -440,7 +440,7 @@ TEST(GncOptimizer, calculateWeightsTLS2) {
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GaussNewtonParams gnParams;
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GncParams<GaussNewtonParams> gncParams(gnParams);
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gncParams.setLossType(
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GncParams<GaussNewtonParams>::RobustLossType::TLS);
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GncParams<GaussNewtonParams>::GncLossType::TLS);
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gncParams.setInlierCostThreshold(0.5); // if inlier threshold is slightly below 0.5, then measurement is outlier
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auto gnc = GncOptimizer<GncParams<GaussNewtonParams>>(nfg, initial, gncParams);
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double mu = 1e6; // very large mu recovers original TLS cost
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@ -552,7 +552,7 @@ TEST(GncOptimizer, optimizeWithKnownInliers) {
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GncParams<GaussNewtonParams> gncParams;
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gncParams.setKnownInliers(knownInliers);
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gncParams.setLossType(
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GncParams<GaussNewtonParams>::RobustLossType::GM);
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GncParams<GaussNewtonParams>::GncLossType::GM);
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//gncParams.setVerbosityGNC(GncParams<GaussNewtonParams>::Verbosity::SUMMARY);
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auto gnc = GncOptimizer<GncParams<GaussNewtonParams>>(fg, initial, gncParams);
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@ -569,7 +569,7 @@ TEST(GncOptimizer, optimizeWithKnownInliers) {
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GncParams<GaussNewtonParams> gncParams;
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gncParams.setKnownInliers(knownInliers);
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gncParams.setLossType(
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GncParams<GaussNewtonParams>::RobustLossType::TLS);
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GncParams<GaussNewtonParams>::GncLossType::TLS);
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// gncParams.setVerbosityGNC(GncParams<GaussNewtonParams>::Verbosity::SUMMARY);
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auto gnc = GncOptimizer<GncParams<GaussNewtonParams>>(fg, initial, gncParams);
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@ -588,7 +588,7 @@ TEST(GncOptimizer, optimizeWithKnownInliers) {
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GncParams<GaussNewtonParams> gncParams;
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gncParams.setKnownInliers(knownInliers);
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gncParams.setLossType(
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GncParams<GaussNewtonParams>::RobustLossType::TLS);
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GncParams<GaussNewtonParams>::GncLossType::TLS);
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//gncParams.setVerbosityGNC(GncParams<GaussNewtonParams>::Verbosity::VALUES);
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gncParams.setInlierCostThreshold( 100.0 );
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auto gnc = GncOptimizer<GncParams<GaussNewtonParams>>(fg, initial, gncParams);
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