12345qiupeng
/
gtsam
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Use new GncParams::IndexVector

release/4.3a0
Varun Agrawal 2022-07-26 07:54:05 -04:00
parent 42bab8f3e7
commit 1d51c4e646
2 changed files with 18 additions and 16 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

12
gtsam/nonlinear/GncParams.h
View File

@ -73,11 +73,13 @@ class GTSAM_EXPORT GncParams {
double weightsTol = 1e-4; ///< If the weights are within weightsTol from being binary, stop iterating (only for TLS)
Verbosity verbosity = SILENT; ///< Verbosity level
// *NOTE* We use KeyVector for inliers and outliers since it is fast + wrapping
//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
KeyVector knownInliers = KeyVector();
IndexVector knownInliers = IndexVector();
///< Slots in the factor graph corresponding to measurements that we know are outliers
KeyVector knownOutliers = KeyVector();
IndexVector knownOutliers = IndexVector();
/// Set the robust loss function to be used in GNC (chosen among the ones in GncLossType).
void setLossType(const GncLossType type) {
@ -118,7 +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 KeyVector& knownIn) {
void setKnownInliers(const IndexVector& knownIn) {
for (size_t i = 0; i < knownIn.size(); i++){
knownInliers.push_back(knownIn[i]);
}
@ -129,7 +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 KeyVector& knownOut) {
void setKnownOutliers(const IndexVector& knownOut) {
for (size_t i = 0; i < knownOut.size(); i++){
knownOutliers.push_back(knownOut[i]);
}

22
tests/testGncOptimizer.cpp
View File

@ -567,7 +567,7 @@ TEST(GncOptimizer, optimizeWithKnownInliers) {
Values initial;
initial.insert(X(1), p0);
std::vector<size_t> knownInliers;
GncParams<GaussNewtonParams>::IndexVector knownInliers;
knownInliers.push_back(0);
knownInliers.push_back(1);
knownInliers.push_back(2);
@ -644,7 +644,7 @@ TEST(GncOptimizer, barcsq) {
Values initial;
initial.insert(X(1), p0);
std::vector<size_t> knownInliers;
GncParams<GaussNewtonParams>::IndexVector knownInliers;
knownInliers.push_back(0);
knownInliers.push_back(1);
knownInliers.push_back(2);
@ -691,7 +691,7 @@ TEST(GncOptimizer, setInlierCostThresholds) {
Values initial;
initial.insert(X(1), p0);
std::vector<size_t> knownInliers;
GncParams<GaussNewtonParams>::IndexVector knownInliers;
knownInliers.push_back(0);
knownInliers.push_back(1);
knownInliers.push_back(2);
@ -763,7 +763,7 @@ TEST(GncOptimizer, optimizeSmallPoseGraph) {
// GNC
// Note: in difficult instances, we set the odometry measurements to be
// inliers, but this problem is simple enought to succeed even without that
// assumption std::vector<size_t> knownInliers;
// assumption GncParams<GaussNewtonParams>::IndexVector knownInliers;
GncParams<GaussNewtonParams> gncParams;
auto gnc = GncOptimizer<GncParams<GaussNewtonParams>>(*graph, *initial,
gncParams);
@ -784,12 +784,12 @@ TEST(GncOptimizer, knownInliersAndOutliers) {
// nonconvexity with known inliers and known outliers (check early stopping
// when all measurements are known to be inliers or outliers)
{
std::vector<size_t> knownInliers;
GncParams<GaussNewtonParams>::IndexVector knownInliers;
knownInliers.push_back(0);
knownInliers.push_back(1);
knownInliers.push_back(2);
std::vector<size_t> knownOutliers;
GncParams<GaussNewtonParams>::IndexVector knownOutliers;
knownOutliers.push_back(3);
GncParams<GaussNewtonParams> gncParams;
@ -813,11 +813,11 @@ TEST(GncOptimizer, knownInliersAndOutliers) {
// nonconvexity with known inliers and known outliers
{
std::vector<size_t> knownInliers;
GncParams<GaussNewtonParams>::IndexVector knownInliers;
knownInliers.push_back(2);
knownInliers.push_back(0);
std::vector<size_t> knownOutliers;
GncParams<GaussNewtonParams>::IndexVector knownOutliers;
knownOutliers.push_back(3);
GncParams<GaussNewtonParams> gncParams;
@ -841,7 +841,7 @@ TEST(GncOptimizer, knownInliersAndOutliers) {
// only known outliers
{
std::vector<size_t> knownOutliers;
GncParams<GaussNewtonParams>::IndexVector knownOutliers;
knownOutliers.push_back(3);
GncParams<GaussNewtonParams> gncParams;
@ -916,11 +916,11 @@ TEST(GncOptimizer, setWeights) {
// initialize weights and also set known inliers/outliers
{
GncParams<GaussNewtonParams> gncParams;
std::vector<size_t> knownInliers;
GncParams<GaussNewtonParams>::IndexVector knownInliers;
knownInliers.push_back(2);
knownInliers.push_back(0);
std::vector<size_t> knownOutliers;
GncParams<GaussNewtonParams>::IndexVector knownOutliers;
knownOutliers.push_back(3);
gncParams.setKnownInliers(knownInliers);
gncParams.setKnownOutliers(knownOutliers);