ParticleFilter.h
Go to the documentation of this file.
1 /*
2 Copyright (c) 2010-2016, Mathieu Labbe - IntRoLab - Universite de Sherbrooke
3 All rights reserved.
4 
5 Redistribution and use in source and binary forms, with or without
6 modification, are permitted provided that the following conditions are met:
7  * Redistributions of source code must retain the above copyright
8  notice, this list of conditions and the following disclaimer.
9  * Redistributions in binary form must reproduce the above copyright
10  notice, this list of conditions and the following disclaimer in the
11  documentation and/or other materials provided with the distribution.
12  * Neither the name of the Universite de Sherbrooke nor the
13  names of its contributors may be used to endorse or promote products
14  derived from this software without specific prior written permission.
15 
16 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
17 ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
18 WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
19 DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY
20 DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
21 (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
22 LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
23 ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
24 (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
25 SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
26 */
27 
28 #ifndef PARTICLEFILTER_H_
29 #define PARTICLEFILTER_H_
30 
31 #include <rtabmap/utilite/UMath.h>
33 
34 
35 namespace rtabmap {
36 
37 // taken from http://www.developpez.net/forums/d544518/c-cpp/c/equivalent-randn-matlab-c/
38 #define TWOPI (6.2831853071795864769252867665590057683943387987502) /* 2 * pi */
39 
40 /*
41  RAND is a macro which returns a pseudo-random numbers from a uniform
42  distribution on the interval [0 1]
43 */
44 #define RAND (rand())/((double) RAND_MAX)
45 
46 /*
47  RANDN is a macro which returns a pseudo-random numbers from a normal
48  distribution with mean zero and standard deviation one. This macro uses Box
49  Muller's algorithm
50 */
51 #define RANDN (sqrt(-2.0*log(RAND))*cos(TWOPI*RAND))
52 
53 std::vector<double> cumSum(const std::vector<double> & v)
54 {
55  std::vector<double> cum(v.size());
56  double sum = 0;
57  for(unsigned int i=0; i<v.size(); ++i)
58  {
59  cum[i] = v[i] + sum;
60  sum += v[i];
61  }
62  return cum;
63 }
64 
65 std::vector<double> resample(const std::vector<double> & p, // particles
66  const std::vector<double> & w, // weights
67  bool normalizeWeights = false)
68 {
69  std::vector<double> np; //new particles
70  if(p.size() != w.size() || p.size() == 0)
71  {
72  UERROR("particles (%d) and weights (%d) are not the same size", p.size(), w.size());
73  return np;
74  }
75 
76  std::vector<double> cs;
77  if(normalizeWeights)
78  {
79  double wSum = uSum(w);
80  std::vector<double> wNorm(w.size());
81  for(unsigned int i=0; i<w.size(); ++i)
82  {
83  wNorm[i] = w[i]/wSum;
84  }
85  cs = cumSum(wNorm); // cumulative sum
86  }
87  else
88  {
89  cs = cumSum(w); // cumulative sum
90  }
91  for(unsigned int j=0; j<cs.size(); ++j)
92  {
93  cs[j]/=cs.back();
94  }
95 
96  np.resize(p.size());
97  for(unsigned int i=0; i<np.size(); ++i)
98  {
99  unsigned int index = 0;
100  double randnum = RAND;
101  for(unsigned int j=0; j<cs.size(); ++j)
102  {
103  if(randnum < cs[j])
104  {
105  index = j;
106  break;
107  }
108  }
109  np[i] = p[index];
110  }
111  return np;
112 }
113 
114 
116 {
117 public:
118  ParticleFilter(unsigned int nParticles = 200,
119  double noise = 0.1,
120  double lambda = 10.0,
121  double initValue = 0.0) :
122  noise_(noise),
123  lambda_(lambda)
124  {
125  particles_.resize(nParticles, initValue);
126  }
127 
128  void init(double initValue = 0.0f)
129  {
130  particles_ = std::vector<double>(particles_.size(), initValue);
131  }
132 
133  double filter(double val)
134  {
135  std::vector<double> weights(particles_.size(), 1);
136  double sumWeights = 0;
137  for(unsigned int i=0; i<particles_.size(); ++i)
138  {
139  // add noise to particle
140  particles_[i] += noise_ * RANDN;
141 
142  // compute weight
143  double dist = fabs(particles_[i] - val);
144  //dist = sqrt(dist*dist);
145  double w = exp(-lambda_*dist);
146  if(uIsFinite(w) && w > 0)
147  {
148  weights[i] = w;
149  }
150  sumWeights += weights[i];
151  }
152 
153 
154  //normalize and compute estimated value
155  double value =0.0;
156  for(unsigned int i=0; i<weights.size(); ++i)
157  {
158  weights[i] /= sumWeights;
159  value += weights[i] * particles_[i];
160  }
161 
162  //resample the particles
163  particles_ = resample(particles_, weights, false);
164 
165  return value;
166  }
167 
168 private:
169  std::vector<double> particles_;
170  double noise_;
171  double lambda_;
172 };
173 
174 }
175 
176 
177 #endif /* PARTICLEFILTER_H_ */
void init(double initValue=0.0f)
double filter(double val)
#define RAND
Basic mathematics functions.
bool uIsFinite(const T &value)
Definition: UMath.h:55
std::vector< double > resample(const std::vector< double > &p, const std::vector< double > &w, bool normalizeWeights=false)
ParticleFilter(unsigned int nParticles=200, double noise=0.1, double lambda=10.0, double initValue=0.0)
std::vector< double > cumSum(const std::vector< double > &v)
GLM_FUNC_DECL genType exp(genType const &x)
#define UERROR(...)
ULogger class and convenient macros.
T uSum(const std::list< T > &list)
Definition: UMath.h:320
std::vector< double > particles_
#define RANDN


rtabmap
Author(s): Mathieu Labbe
autogenerated on Mon Dec 14 2020 03:34:59