24navigation/install/octomap-distribution/include/octomap/AbstractOccupancyOcTree.h

242 lines
10 KiB
C++

/*
* OctoMap - An Efficient Probabilistic 3D Mapping Framework Based on Octrees
* https://octomap.github.io/
*
* Copyright (c) 2009-2013, K.M. Wurm and A. Hornung, University of Freiburg
* All rights reserved.
* License: New BSD
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the University of Freiburg nor the names of its
* contributors may be used to endorse or promote products derived from
* this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
* ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
* LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
* CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
* SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
* INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
* CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
* ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
#ifndef OCTOMAP_ABSTRACT_OCCUPANCY_OCTREE_H
#define OCTOMAP_ABSTRACT_OCCUPANCY_OCTREE_H
#include "AbstractOcTree.h"
#include "octomap_utils.h"
#include "OcTreeNode.h"
#include "OcTreeKey.h"
#include <cassert>
#include <fstream>
namespace octomap {
/**
* Interface class for all octree types that store occupancy. This serves
* as a common base class e.g. for polymorphism and contains common code
* for reading and writing binary trees.
*/
class AbstractOccupancyOcTree : public AbstractOcTree {
public:
AbstractOccupancyOcTree();
virtual ~AbstractOccupancyOcTree() {}
//-- IO
/**
* Writes OcTree to a binary file using writeBinary().
* The OcTree is first converted to the maximum likelihood estimate and pruned.
* @return success of operation
*/
bool writeBinary(const std::string& filename);
/**
* Writes compressed maximum likelihood OcTree to a binary stream.
* The OcTree is first converted to the maximum likelihood estimate and pruned
* for maximum compression.
* @return success of operation
*/
bool writeBinary(std::ostream &s);
/**
* Writes OcTree to a binary file using writeBinaryConst().
* The OcTree is not changed, in particular not pruned first.
* Files will be smaller when the tree is pruned first or by using
* writeBinary() instead.
* @return success of operation
*/
bool writeBinaryConst(const std::string& filename) const;
/**
* Writes the maximum likelihood OcTree to a binary stream (const variant).
* Files will be smaller when the tree is pruned first or by using
* writeBinary() instead.
* @return success of operation
*/
bool writeBinaryConst(std::ostream &s) const;
/// Writes the actual data, implemented in OccupancyOcTreeBase::writeBinaryData()
virtual std::ostream& writeBinaryData(std::ostream &s) const = 0;
/**
* Reads an OcTree from an input stream.
* Existing nodes of the tree are deleted before the tree is read.
* @return success of operation
*/
bool readBinary(std::istream &s);
/**
* Reads OcTree from a binary file.
* Existing nodes of the tree are deleted before the tree is read.
* @return success of operation
*/
bool readBinary(const std::string& filename);
/// Reads the actual data, implemented in OccupancyOcTreeBase::readBinaryData()
virtual std::istream& readBinaryData(std::istream &s) = 0;
// -- occupancy queries
/// queries whether a node is occupied according to the tree's parameter for "occupancy"
inline bool isNodeOccupied(const OcTreeNode* occupancyNode) const{
return (occupancyNode->getLogOdds() >= this->occ_prob_thres_log);
}
/// queries whether a node is occupied according to the tree's parameter for "occupancy"
inline bool isNodeOccupied(const OcTreeNode& occupancyNode) const{
return (occupancyNode.getLogOdds() >= this->occ_prob_thres_log);
}
/// queries whether a node is at the clamping threshold according to the tree's parameter
inline bool isNodeAtThreshold(const OcTreeNode* occupancyNode) const{
return (occupancyNode->getLogOdds() >= this->clamping_thres_max
|| occupancyNode->getLogOdds() <= this->clamping_thres_min);
}
/// queries whether a node is at the clamping threshold according to the tree's parameter
inline bool isNodeAtThreshold(const OcTreeNode& occupancyNode) const{
return (occupancyNode.getLogOdds() >= this->clamping_thres_max
|| occupancyNode.getLogOdds() <= this->clamping_thres_min);
}
// - update functions
/**
* Manipulate log_odds value of voxel directly
*
* @param key of the NODE that is to be updated
* @param log_odds_update value to be added (+) to log_odds value of node
* @param lazy_eval whether update of inner nodes is omitted after the update (default: false).
* This speeds up the insertion, but you need to call updateInnerOccupancy() when done.
* @return pointer to the updated NODE
*/
virtual OcTreeNode* updateNode(const OcTreeKey& key, float log_odds_update, bool lazy_eval = false) = 0;
/**
* Manipulate log_odds value of voxel directly.
* Looks up the OcTreeKey corresponding to the coordinate and then calls udpateNode() with it.
*
* @param value 3d coordinate of the NODE that is to be updated
* @param log_odds_update value to be added (+) to log_odds value of node
* @param lazy_eval whether update of inner nodes is omitted after the update (default: false).
* This speeds up the insertion, but you need to call updateInnerOccupancy() when done.
* @return pointer to the updated NODE
*/
virtual OcTreeNode* updateNode(const point3d& value, float log_odds_update, bool lazy_eval = false) = 0;
/**
* Integrate occupancy measurement.
*
* @param key of the NODE that is to be updated
* @param occupied true if the node was measured occupied, else false
* @param lazy_eval whether update of inner nodes is omitted after the update (default: false).
* This speeds up the insertion, but you need to call updateInnerOccupancy() when done.
* @return pointer to the updated NODE
*/
virtual OcTreeNode* updateNode(const OcTreeKey& key, bool occupied, bool lazy_eval = false) = 0;
/**
* Integrate occupancy measurement.
* Looks up the OcTreeKey corresponding to the coordinate and then calls udpateNode() with it.
*
* @param value 3d coordinate of the NODE that is to be updated
* @param occupied true if the node was measured occupied, else false
* @param lazy_eval whether update of inner nodes is omitted after the update (default: false).
* This speeds up the insertion, but you need to call updateInnerOccupancy() when done.
* @return pointer to the updated NODE
*/
virtual OcTreeNode* updateNode(const point3d& value, bool occupied, bool lazy_eval = false) = 0;
virtual void toMaxLikelihood() = 0;
//-- parameters for occupancy and sensor model:
/// sets the threshold for occupancy (sensor model)
void setOccupancyThres(double prob){occ_prob_thres_log = logodds(prob); }
/// sets the probability for a "hit" (will be converted to logodds) - sensor model
void setProbHit(double prob){prob_hit_log = logodds(prob); assert(prob_hit_log >= 0.0);}
/// sets the probability for a "miss" (will be converted to logodds) - sensor model
void setProbMiss(double prob){prob_miss_log = logodds(prob); assert(prob_miss_log <= 0.0);}
/// sets the minimum threshold for occupancy clamping (sensor model)
void setClampingThresMin(double thresProb){clamping_thres_min = logodds(thresProb); }
/// sets the maximum threshold for occupancy clamping (sensor model)
void setClampingThresMax(double thresProb){clamping_thres_max = logodds(thresProb); }
/// @return threshold (probability) for occupancy - sensor model
double getOccupancyThres() const {return probability(occ_prob_thres_log); }
/// @return threshold (logodds) for occupancy - sensor model
float getOccupancyThresLog() const {return occ_prob_thres_log; }
/// @return probability for a "hit" in the sensor model (probability)
double getProbHit() const {return probability(prob_hit_log); }
/// @return probability for a "hit" in the sensor model (logodds)
float getProbHitLog() const {return prob_hit_log; }
/// @return probability for a "miss" in the sensor model (probability)
double getProbMiss() const {return probability(prob_miss_log); }
/// @return probability for a "miss" in the sensor model (logodds)
float getProbMissLog() const {return prob_miss_log; }
/// @return minimum threshold for occupancy clamping in the sensor model (probability)
double getClampingThresMin() const {return probability(clamping_thres_min); }
/// @return minimum threshold for occupancy clamping in the sensor model (logodds)
float getClampingThresMinLog() const {return clamping_thres_min; }
/// @return maximum threshold for occupancy clamping in the sensor model (probability)
double getClampingThresMax() const {return probability(clamping_thres_max); }
/// @return maximum threshold for occupancy clamping in the sensor model (logodds)
float getClampingThresMaxLog() const {return clamping_thres_max; }
protected:
/// Try to read the old binary format for conversion, will be removed in the future
bool readBinaryLegacyHeader(std::istream &s, unsigned int& size, double& res);
// occupancy parameters of tree, stored in logodds:
float clamping_thres_min;
float clamping_thres_max;
float prob_hit_log;
float prob_miss_log;
float occ_prob_thres_log;
static const std::string binaryFileHeader;
};
} // end namespace
#endif