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// ----------------------------------------------------------------------------
// - Open3D: www.open3d.org -
// ----------------------------------------------------------------------------
// The MIT License (MIT)
//
// Copyright (c) 2018-2021 www.open3d.org
//
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the Software is
// furnished to do so, subject to the following conditions:
//
// The above copyright notice and this permission notice shall be included in
// all copies or substantial portions of the Software.
//
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
// IN THE SOFTWARE.
// ----------------------------------------------------------------------------
#include "open3d/pipelines/registration/GlobalOptimization.h"
#include "open3d/pipelines/registration/GlobalOptimizationConvergenceCriteria.h"
#include "open3d/pipelines/registration/GlobalOptimizationMethod.h"
#include "open3d/pipelines/registration/PoseGraph.h"
#include "pybind/docstring.h"
#include "pybind/pipelines/registration/registration.h"
namespace open3d {
namespace pipelines {
namespace registration {
template <class GlobalOptimizationMethodBase = GlobalOptimizationMethod>
class PyGlobalOptimizationMethod : public GlobalOptimizationMethodBase {
public:
using GlobalOptimizationMethodBase::GlobalOptimizationMethodBase;
void OptimizePoseGraph(
PoseGraph &pose_graph,
const GlobalOptimizationConvergenceCriteria &criteria,
const GlobalOptimizationOption &option) const override {
PYBIND11_OVERLOAD_PURE(void, GlobalOptimizationMethodBase, pose_graph,
criteria, option);
}
};
void pybind_global_optimization(py::module &m) {
// open3d.registration.PoseGraphNode
py::class_<PoseGraphNode, std::shared_ptr<PoseGraphNode>> pose_graph_node(
m, "PoseGraphNode", "Node of ``PoseGraph``.");
py::detail::bind_default_constructor<PoseGraphNode>(pose_graph_node);
py::detail::bind_copy_functions<PoseGraphNode>(pose_graph_node);
pose_graph_node.def_readwrite("pose", &PoseGraphNode::pose_)
.def(py::init([](Eigen::Matrix4d pose =
Eigen::Matrix4d::Identity()) {
return new PoseGraphNode(pose);
}),
"pose"_a)
.def("__repr__", [](const PoseGraphNode &rr) {
return std::string(
"PoseGraphNode, access "
"pose to get its "
"current pose.");
});
// open3d.registration.PoseGraphNodeVector
auto pose_graph_node_vector = py::bind_vector<std::vector<PoseGraphNode>>(
m, "PoseGraphNodeVector");
pose_graph_node_vector.attr("__doc__") = docstring::static_property(
py::cpp_function([](py::handle arg) -> std::string {
return "Vector of PoseGraphNode";
}),
py::none(), py::none(), "");
// open3d.registration.PoseGraphEdge
py::class_<PoseGraphEdge, std::shared_ptr<PoseGraphEdge>> pose_graph_edge(
m, "PoseGraphEdge", "Edge of ``PoseGraph``.");
py::detail::bind_default_constructor<PoseGraphEdge>(pose_graph_edge);
py::detail::bind_copy_functions<PoseGraphEdge>(pose_graph_edge);
pose_graph_edge
.def_readwrite("source_node_id", &PoseGraphEdge::source_node_id_,
"int: Source ``PoseGraphNode`` id.")
.def_readwrite("target_node_id", &PoseGraphEdge::target_node_id_,
"int: Target ``PoseGraphNode`` id.")
.def_readwrite(
"transformation", &PoseGraphEdge::transformation_,
"``4 x 4`` float64 numpy array: Transformation matrix.")
.def_readwrite("information", &PoseGraphEdge::information_,
"``6 x 6`` float64 numpy array: Information matrix.")
.def_readwrite("uncertain", &PoseGraphEdge::uncertain_,
"bool: Whether the edge is uncertain. Odometry edge "
"has uncertain == false, loop closure edges has "
"uncertain == true")
.def_readwrite(
"confidence", &PoseGraphEdge::confidence_,
"float from 0 to 1: Confidence value of the edge. if "
"uncertain is true, it has confidence bounded in [0,1]. "
"1 means reliable, and 0 means "
"unreliable edge. This correspondence to "
"line process value in [Choi et al 2015] See "
"core/registration/globaloptimization.h for more details.")
.def(py::init([](int source_node_id, int target_node_id,
Eigen::Matrix4d transformation,
Eigen::Matrix6d information, bool uncertain,
double confidence) {
return new PoseGraphEdge(source_node_id, target_node_id,
transformation, information,
uncertain, confidence);
}),
"source_node_id"_a = -1, "target_node_id"_a = -1,
"transformation"_a = Eigen::Matrix4d::Identity(),
"information"_a = Eigen::Matrix6d::Identity(),
"uncertain"_a = false, "confidence"_a = 1.0)
.def("__repr__", [](const PoseGraphEdge &rr) {
return std::string(
"PoseGraphEdge "
"from nodes ") +
std::to_string(rr.source_node_id_) +
std::string(" to ") +
std::to_string(rr.target_node_id_) +
std::string(
", access transformation to get relative "
"transformation");
});
// open3d.registration.PoseGraphEdgeVector
auto pose_graph_edge_vector = py::bind_vector<std::vector<PoseGraphEdge>>(
m, "PoseGraphEdgeVector");
pose_graph_edge_vector.attr("__doc__") = docstring::static_property(
py::cpp_function([](py::handle arg) -> std::string {
return "Vector of PoseGraphEdge";
}),
py::none(), py::none(), "");
// open3d.registration.PoseGraph
py::class_<PoseGraph, std::shared_ptr<PoseGraph>> pose_graph(
m, "PoseGraph", "Data structure defining the pose graph.");
py::detail::bind_default_constructor<PoseGraph>(pose_graph);
py::detail::bind_copy_functions<PoseGraph>(pose_graph);
pose_graph
.def_readwrite(
"nodes", &PoseGraph::nodes_,
"``List(PoseGraphNode)``: List of ``PoseGraphNode``.")
.def_readwrite(
"edges", &PoseGraph::edges_,
"``List(PoseGraphEdge)``: List of ``PoseGraphEdge``.")
.def("__repr__", [](const PoseGraph &rr) {
return std::string("PoseGraph with ") +
std::to_string(rr.nodes_.size()) +
std::string(" nodes and ") +
std::to_string(rr.edges_.size()) +
std::string(" edges.");
});
// open3d.registration.GlobalOptimizationMethod
py::class_<GlobalOptimizationMethod,
PyGlobalOptimizationMethod<GlobalOptimizationMethod>>
global_optimization_method(
m, "GlobalOptimizationMethod",
"Base class for global optimization method.");
global_optimization_method.def("OptimizePoseGraph",
&GlobalOptimizationMethod::OptimizePoseGraph,
"pose_graph"_a, "criteria"_a, "option"_a,
"Run pose graph optimization.");
docstring::ClassMethodDocInject(
m, "GlobalOptimizationMethod", "OptimizePoseGraph",
{{"pose_graph", "The pose graph to be optimized (in-place)."},
{"criteria", "Convergence criteria."},
{"option", "Global optimization options."}});
py::class_<GlobalOptimizationLevenbergMarquardt,
PyGlobalOptimizationMethod<GlobalOptimizationLevenbergMarquardt>,
GlobalOptimizationMethod>
global_optimization_method_lm(
m, "GlobalOptimizationLevenbergMarquardt",
"Global optimization with Levenberg-Marquardt algorithm. "
"Recommended over the Gauss-Newton method since the LM has "
"better convergence characteristics.");
py::detail::bind_default_constructor<GlobalOptimizationLevenbergMarquardt>(
global_optimization_method_lm);
py::detail::bind_copy_functions<GlobalOptimizationLevenbergMarquardt>(
global_optimization_method_lm);
global_optimization_method_lm.def(
"__repr__", [](const GlobalOptimizationLevenbergMarquardt &te) {
return std::string("GlobalOptimizationLevenbergMarquardt");
});
py::class_<GlobalOptimizationGaussNewton,
PyGlobalOptimizationMethod<GlobalOptimizationGaussNewton>,
GlobalOptimizationMethod>
global_optimization_method_gn(
m, "GlobalOptimizationGaussNewton",
"Global optimization with Gauss-Newton algorithm.");
py::detail::bind_default_constructor<GlobalOptimizationGaussNewton>(
global_optimization_method_gn);
py::detail::bind_copy_functions<GlobalOptimizationGaussNewton>(
global_optimization_method_gn);
global_optimization_method_gn.def(
"__repr__", [](const GlobalOptimizationGaussNewton &te) {
return std::string("GlobalOptimizationGaussNewton");
});
py::class_<GlobalOptimizationConvergenceCriteria> criteria(
m, "GlobalOptimizationConvergenceCriteria",
"Convergence criteria of GlobalOptimization.");
py::detail::bind_default_constructor<GlobalOptimizationConvergenceCriteria>(
criteria);
py::detail::bind_copy_functions<GlobalOptimizationConvergenceCriteria>(
criteria);
criteria.def_readwrite(
"max_iteration",
&GlobalOptimizationConvergenceCriteria::max_iteration_,
"int: Maximum iteration number for iterative optimization "
"module.")
.def_readwrite("min_relative_increment",
&GlobalOptimizationConvergenceCriteria::
min_relative_increment_,
"float: Minimum relative increments.")
.def_readwrite("min_relative_residual_increment",
&GlobalOptimizationConvergenceCriteria::
min_relative_residual_increment_,
"float: Minimum relative residual increments.")
.def_readwrite(
"min_right_term",
&GlobalOptimizationConvergenceCriteria::min_right_term_,
"float: Minimum right term value.")
.def_readwrite(
"min_residual",
&GlobalOptimizationConvergenceCriteria::min_residual_,
"float: Minimum residual value.")
.def_readwrite(
"max_iteration_lm",
&GlobalOptimizationConvergenceCriteria::max_iteration_lm_,
"int: Maximum iteration number for Levenberg Marquardt "
"method. max_iteration_lm is used for additional "
"Levenberg-Marquardt inner loop that automatically changes "
"steepest gradient gain.")
.def_readwrite(
"upper_scale_factor",
&GlobalOptimizationConvergenceCriteria::upper_scale_factor_,
"float: Upper scale factor value. Scaling factors are used "
"for levenberg marquardt algorithm these are scaling "
"factors that increase/decrease lambda used in H_LM = H + "
"lambda * I")
.def_readwrite(
"lower_scale_factor",
&GlobalOptimizationConvergenceCriteria::lower_scale_factor_,
"float: Lower scale factor value.")
.def("__repr__", [](const GlobalOptimizationConvergenceCriteria
&cr) {
return std::string("GlobalOptimizationConvergenceCriteria") +
std::string("\n> max_iteration : ") +
std::to_string(cr.max_iteration_) +
std::string("\n> min_relative_increment : ") +
std::to_string(cr.min_relative_increment_) +
std::string("\n> min_relative_residual_increment : ") +
std::to_string(cr.min_relative_residual_increment_) +
std::string("\n> min_right_term : ") +
std::to_string(cr.min_right_term_) +
std::string("\n> min_residual : ") +
std::to_string(cr.min_residual_) +
std::string("\n> max_iteration_lm : ") +
std::to_string(cr.max_iteration_lm_) +
std::string("\n> upper_scale_factor : ") +
std::to_string(cr.upper_scale_factor_) +
std::string("\n> lower_scale_factor : ") +
std::to_string(cr.lower_scale_factor_);
});
py::class_<GlobalOptimizationOption> option(
m, "GlobalOptimizationOption", "Option for GlobalOptimization.");
py::detail::bind_default_constructor<GlobalOptimizationOption>(option);
py::detail::bind_copy_functions<GlobalOptimizationOption>(option);
option.def_readwrite(
"max_correspondence_distance",
&GlobalOptimizationOption::max_correspondence_distance_,
"float: Identifies which distance value is used for "
"finding neighboring points when making information "
"matrix. According to [Choi et al 2015], this "
"distance is used for determining $mu, a line process "
"weight.")
.def_readwrite("edge_prune_threshold",
&GlobalOptimizationOption::edge_prune_threshold_,
"float: According to [Choi et al 2015], "
"line_process weight < edge_prune_threshold (0.25) "
"is pruned.")
.def_readwrite("preference_loop_closure",
&GlobalOptimizationOption::preference_loop_closure_,
"float: Balancing parameter to decide which one is "
"more reliable: odometry vs loop-closure. [0,1] -> "
"try to unchange odometry edges, [1) -> try to "
"utilize loop-closure. Recommendation: 0.1 for RGBD "
"Odometry, 2.0 for fragment registration.")
.def_readwrite("reference_node",
&GlobalOptimizationOption::reference_node_,
"int: The pose of this node is unchanged after "
"optimization.")
.def(py::init([](double max_correspondence_distance,
double edge_prune_threshold,
double preference_loop_closure,
int reference_node) {
return new GlobalOptimizationOption(
max_correspondence_distance, edge_prune_threshold,
preference_loop_closure, reference_node);
}),
"max_correspondence_distance"_a = 0.03,
"edge_prune_threshold"_a = 0.25,
"preference_loop_closure"_a = 1.0, "reference_node"_a = -1)
.def("__repr__", [](const GlobalOptimizationOption &goo) {
return std::string("GlobalOptimizationOption") +
std::string("\n> max_correspondence_distance : ") +
std::to_string(goo.max_correspondence_distance_) +
std::string("\n> edge_prune_threshold : ") +
std::to_string(goo.edge_prune_threshold_) +
std::string("\n> preference_loop_closure : ") +
std::to_string(goo.preference_loop_closure_) +
std::string("\n> reference_node : ") +
std::to_string(goo.reference_node_);
});
}
void pybind_global_optimization_methods(py::module &m) {
m.def(
"global_optimization",
[](PoseGraph &pose_graph, const GlobalOptimizationMethod &method,
const GlobalOptimizationConvergenceCriteria &criteria,
const GlobalOptimizationOption &option) {
GlobalOptimization(pose_graph, method, criteria, option);
},
"Function to optimize PoseGraph", "pose_graph"_a, "method"_a,
"criteria"_a, "option"_a);
docstring::FunctionDocInject(
m, "global_optimization",
{{"pose_graph", "The pose_graph to be optimized (in-place)."},
{"method",
"Global optimization method. Either "
"``GlobalOptimizationGaussNewton()`` or "
"``GlobalOptimizationLevenbergMarquardt("
")``."},
{"criteria", "Global optimization convergence criteria."},
{"option", "Global optimization option."}});
}
} // namespace registration
} // namespace pipelines
} // namespace open3d
|
