amcl_laser.cpp

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/*
 *  Player - One Hell of a Robot Server
 *  Copyright (C) 2000  Brian Gerkey   &  Kasper Stoy
 *                      gerkey@usc.edu    kaspers@robotics.usc.edu
 *
 *  This library is free software; you can redistribute it and/or
 *  modify it under the terms of the GNU Lesser General Public
 *  License as published by the Free Software Foundation; either
 *  version 2.1 of the License, or (at your option) any later version.
 *
 *  This library is distributed in the hope that it will be useful,
 *  but WITHOUT ANY WARRANTY; without even the implied warranty of
 *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
 *  Lesser General Public License for more details.
 *
 *  You should have received a copy of the GNU Lesser General Public
 *  License along with this library; if not, write to the Free Software
 *  Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
 *
 */
//
// Desc: AMCL laser routines
// Author: Andrew Howard
// Date: 6 Feb 2003
// CVS: $Id: amcl_laser.cc 7057 2008-10-02 00:44:06Z gbiggs $
//
 
#include <sys/types.h> // required by Darwin
#include <math.h>
#include <stdlib.h>
#include <assert.h>
#ifdef HAVE_UNISTD_H
#include <unistd.h>
#endif
 
#include "amcl/sensors/amcl_laser.h"
 
using namespace amcl;
 
// Default constructor
AMCLLaser::AMCLLaser(size_t max_beams, map_t* map) : AMCLSensor(), 
                                                     max_samples(0), max_obs(0), 
                                                     temp_obs(NULL)
{
  this->time = 0.0;
 
  this->max_beams = max_beams;
  this->map = map;
 
  return;
}
 
AMCLLaser::~AMCLLaser()
{
  if(temp_obs){
        for(int k=0; k < max_samples; k++){
          delete [] temp_obs[k];
        }
        delete []temp_obs; 
  }
}
 
void 
AMCLLaser::SetModelBeam(double z_hit,
                        double z_short,
                        double z_max,
                        double z_rand,
                        double sigma_hit,
                        double lambda_short,
                        double chi_outlier)
{
  this->model_type = LASER_MODEL_BEAM;
  this->z_hit = z_hit;
  this->z_short = z_short;
  this->z_max = z_max;
  this->z_rand = z_rand;
  this->sigma_hit = sigma_hit;
  this->lambda_short = lambda_short;
  this->chi_outlier = chi_outlier;
}
 
void 
AMCLLaser::SetModelLikelihoodField(double z_hit,
                                   double z_rand,
                                   double sigma_hit,
                                   double max_occ_dist)
{
  this->model_type = LASER_MODEL_LIKELIHOOD_FIELD;
  this->z_hit = z_hit;
  this->z_rand = z_rand;
  this->sigma_hit = sigma_hit;
 
  map_update_cspace(this->map, max_occ_dist);
}
 
void 
AMCLLaser::SetModelLikelihoodFieldProb(double z_hit,
                                       double z_rand,
                                       double sigma_hit,
                                       double max_occ_dist,
                                       bool do_beamskip,
                                       double beam_skip_distance,
                                       double beam_skip_threshold, 
                                       double beam_skip_error_threshold)
{
  this->model_type = LASER_MODEL_LIKELIHOOD_FIELD_PROB;
  this->z_hit = z_hit;
  this->z_rand = z_rand;
  this->sigma_hit = sigma_hit;
  this->do_beamskip = do_beamskip;
  this->beam_skip_distance = beam_skip_distance;
  this->beam_skip_threshold = beam_skip_threshold;
  this->beam_skip_error_threshold = beam_skip_error_threshold;
  map_update_cspace(this->map, max_occ_dist);
}
 
 
// Apply the laser sensor model
bool AMCLLaser::UpdateSensor(pf_t *pf, AMCLSensorData *data)
{
  if (this->max_beams < 2)
    return false;
 
  // Apply the laser sensor model
  if(this->model_type == LASER_MODEL_BEAM)
    pf_update_sensor(pf, (pf_sensor_model_fn_t) BeamModel, data);
  else if(this->model_type == LASER_MODEL_LIKELIHOOD_FIELD)
    pf_update_sensor(pf, (pf_sensor_model_fn_t) LikelihoodFieldModel, data);  
  else if(this->model_type == LASER_MODEL_LIKELIHOOD_FIELD_PROB)
    pf_update_sensor(pf, (pf_sensor_model_fn_t) LikelihoodFieldModelProb, data);  
  else
    pf_update_sensor(pf, (pf_sensor_model_fn_t) BeamModel, data);
 
  return true;
}
 
 
// Determine the probability for the given pose
double AMCLLaser::BeamModel(AMCLLaserData *data, pf_sample_set_t* set)
{
  AMCLLaser *self;
  int i, j, step;
  double z, pz;
  double p;
  double map_range;
  double obs_range, obs_bearing;
  double total_weight;
  pf_sample_t *sample;
  pf_vector_t pose;
 
  self = (AMCLLaser*) data->sensor;
 
  total_weight = 0.0;
 
  // Compute the sample weights
  for (j = 0; j < set->sample_count; j++)
  {
    sample = set->samples + j;
    pose = sample->pose;
 
    // Take account of the laser pose relative to the robot
    pose = pf_vector_coord_add(self->laser_pose, pose);
 
    p = 1.0;
 
    step = (data->range_count - 1) / (self->max_beams - 1);
    for (i = 0; i < data->range_count; i += step)
    {
      obs_range = data->ranges[i][0];
      obs_bearing = data->ranges[i][1];
 
      // Compute the range according to the map
      map_range = map_calc_range(self->map, pose.v[0], pose.v[1],
                                 pose.v[2] + obs_bearing, data->range_max);
      pz = 0.0;
 
      // Part 1: good, but noisy, hit
      z = obs_range - map_range;
      pz += self->z_hit * exp(-(z * z) / (2 * self->sigma_hit * self->sigma_hit));
 
      // Part 2: short reading from unexpected obstacle (e.g., a person)
      if(z < 0)
        pz += self->z_short * self->lambda_short * exp(-self->lambda_short*obs_range);
 
      // Part 3: Failure to detect obstacle, reported as max-range
      if(obs_range == data->range_max)
        pz += self->z_max * 1.0;
 
      // Part 4: Random measurements
      if(obs_range < data->range_max)
        pz += self->z_rand * 1.0/data->range_max;
 
      // TODO: outlier rejection for short readings
 
      assert(pz <= 1.0);
      assert(pz >= 0.0);
      //      p *= pz;
      // here we have an ad-hoc weighting scheme for combining beam probs
      // works well, though...
      p += pz*pz*pz;
    }
 
    sample->weight *= p;
    total_weight += sample->weight;
  }
 
  return(total_weight);
}
 
double AMCLLaser::LikelihoodFieldModel(AMCLLaserData *data, pf_sample_set_t* set)
{
  AMCLLaser *self;
  int i, j, step;
  double z, pz;
  double p;
  double obs_range, obs_bearing;
  double total_weight;
  pf_sample_t *sample;
  pf_vector_t pose;
  pf_vector_t hit;
 
  self = (AMCLLaser*) data->sensor;
 
  total_weight = 0.0;
 
  // Compute the sample weights
  for (j = 0; j < set->sample_count; j++)
  {
    sample = set->samples + j;
    pose = sample->pose;
 
    // Take account of the laser pose relative to the robot
    pose = pf_vector_coord_add(self->laser_pose, pose);
 
    p = 1.0;
 
    // Pre-compute a couple of things
    double z_hit_denom = 2 * self->sigma_hit * self->sigma_hit;
    double z_rand_mult = 1.0/data->range_max;
 
    step = (data->range_count - 1) / (self->max_beams - 1);
 
    // Step size must be at least 1
    if(step < 1)
      step = 1;
 
    for (i = 0; i < data->range_count; i += step)
    {
      obs_range = data->ranges[i][0];
      obs_bearing = data->ranges[i][1];
 
      // This model ignores max range readings
      if(obs_range >= data->range_max)
        continue;
 
      // Check for NaN
      if(obs_range != obs_range)
        continue;
 
      pz = 0.0;
 
      // Compute the endpoint of the beam
      hit.v[0] = pose.v[0] + obs_range * cos(pose.v[2] + obs_bearing);
      hit.v[1] = pose.v[1] + obs_range * sin(pose.v[2] + obs_bearing);
 
      // Convert to map grid coords.
      int mi, mj;
      mi = MAP_GXWX(self->map, hit.v[0]);
      mj = MAP_GYWY(self->map, hit.v[1]);
      
      // Part 1: Get distance from the hit to closest obstacle.
      // Off-map penalized as max distance
      if(!MAP_VALID(self->map, mi, mj))
        z = self->map->max_occ_dist;
      else
        z = self->map->cells[MAP_INDEX(self->map,mi,mj)].occ_dist;
      // Gaussian model
      // NOTE: this should have a normalization of 1/(sqrt(2pi)*sigma)
      pz += self->z_hit * exp(-(z * z) / z_hit_denom);
      // Part 2: random measurements
      pz += self->z_rand * z_rand_mult;
 
      // TODO: outlier rejection for short readings
 
      assert(pz <= 1.0);
      assert(pz >= 0.0);
      //      p *= pz;
      // here we have an ad-hoc weighting scheme for combining beam probs
      // works well, though...
      p += pz*pz*pz;
    }
 
    sample->weight *= p;
    total_weight += sample->weight;
  }
 
  return(total_weight);
}
 
double AMCLLaser::LikelihoodFieldModelProb(AMCLLaserData *data, pf_sample_set_t* set)
{
  AMCLLaser *self;
  int i, j, step;
  double z, pz;
  double log_p;
  double obs_range, obs_bearing;
  double total_weight;
  pf_sample_t *sample;
  pf_vector_t pose;
  pf_vector_t hit;
 
  self = (AMCLLaser*) data->sensor;
 
  total_weight = 0.0;
 
  step = ceil((data->range_count) / static_cast<double>(self->max_beams)); 
  
  // Step size must be at least 1
  if(step < 1)
    step = 1;
 
  // Pre-compute a couple of things
  double z_hit_denom = 2 * self->sigma_hit * self->sigma_hit;
  double z_rand_mult = 1.0/data->range_max;
 
  double max_dist_prob = exp(-(self->map->max_occ_dist * self->map->max_occ_dist) / z_hit_denom);
 
  //Beam skipping - ignores beams for which a majority of particles do not agree with the map
  //prevents correct particles from getting down weighted because of unexpected obstacles 
  //such as humans 
 
  bool do_beamskip = self->do_beamskip;
  double beam_skip_distance = self->beam_skip_distance;
  double beam_skip_threshold = self->beam_skip_threshold;
  
  //we only do beam skipping if the filter has converged 
  if(do_beamskip && !set->converged){
    do_beamskip = false;
  }
 
  //we need a count the no of particles for which the beam agreed with the map 
  int *obs_count = new int[self->max_beams]();
 
  //we also need a mask of which observations to integrate (to decide which beams to integrate to all particles) 
  bool *obs_mask = new bool[self->max_beams]();
  
  int beam_ind = 0;
  
  //realloc indicates if we need to reallocate the temp data structure needed to do beamskipping 
  bool realloc = false; 
 
  if(do_beamskip){
    if(self->max_obs < self->max_beams){
      realloc = true;
    }
 
    if(self->max_samples < set->sample_count){
      realloc = true;
    }
 
    if(realloc){
      self->reallocTempData(set->sample_count, self->max_beams);     
      fprintf(stderr, "Reallocing temp weights %d - %d\n", self->max_samples, self->max_obs);
    }
  }
 
  // Compute the sample weights
  for (j = 0; j < set->sample_count; j++)
  {
    sample = set->samples + j;
    pose = sample->pose;
 
    // Take account of the laser pose relative to the robot
    pose = pf_vector_coord_add(self->laser_pose, pose);
 
    log_p = 0;
    
    beam_ind = 0;
    
    for (i = 0; i < data->range_count; i += step, beam_ind++)
    {
      obs_range = data->ranges[i][0];
      obs_bearing = data->ranges[i][1];
 
      // This model ignores max range readings
      if(obs_range >= data->range_max){
        continue;
      }
 
      // Check for NaN
      if(obs_range != obs_range){
        continue;
      }
 
      pz = 0.0;
 
      // Compute the endpoint of the beam
      hit.v[0] = pose.v[0] + obs_range * cos(pose.v[2] + obs_bearing);
      hit.v[1] = pose.v[1] + obs_range * sin(pose.v[2] + obs_bearing);
 
      // Convert to map grid coords.
      int mi, mj;
      mi = MAP_GXWX(self->map, hit.v[0]);
      mj = MAP_GYWY(self->map, hit.v[1]);
      
      // Part 1: Get distance from the hit to closest obstacle.
      // Off-map penalized as max distance
      
      if(!MAP_VALID(self->map, mi, mj)){
        pz += self->z_hit * max_dist_prob;
      }
      else{
        z = self->map->cells[MAP_INDEX(self->map,mi,mj)].occ_dist;
        if(z < beam_skip_distance){
          obs_count[beam_ind] += 1;
        }
        pz += self->z_hit * exp(-(z * z) / z_hit_denom);
      }
       
      // Gaussian model
      // NOTE: this should have a normalization of 1/(sqrt(2pi)*sigma)
      
      // Part 2: random measurements
      pz += self->z_rand * z_rand_mult;
 
      assert(pz <= 1.0); 
      assert(pz >= 0.0);
 
      // TODO: outlier rejection for short readings
            
      if(!do_beamskip){
        log_p += log(pz);
      }
      else{
        self->temp_obs[j][beam_ind] = pz; 
      }
    }
    if(!do_beamskip){
      sample->weight *= exp(log_p);
      total_weight += sample->weight;
    }
  }
  
  if(do_beamskip){
    int skipped_beam_count = 0; 
    for (beam_ind = 0; beam_ind < self->max_beams; beam_ind++){
      if((obs_count[beam_ind] / static_cast<double>(set->sample_count)) > beam_skip_threshold){
        obs_mask[beam_ind] = true;
      }
      else{
        obs_mask[beam_ind] = false;
        skipped_beam_count++; 
      }
    }
 
    //we check if there is at least a critical number of beams that agreed with the map 
    //otherwise it probably indicates that the filter converged to a wrong solution
    //if that's the case we integrate all the beams and hope the filter might converge to 
    //the right solution
    bool error = false; 
 
    if(skipped_beam_count >= (beam_ind * self->beam_skip_error_threshold)){
      fprintf(stderr, "Over %f%% of the observations were not in the map - pf may have converged to wrong pose - integrating all observations\n", (100 * self->beam_skip_error_threshold));
      error = true; 
    }
 
    for (j = 0; j < set->sample_count; j++)
      {
        sample = set->samples + j;
        pose = sample->pose;
 
        log_p = 0;
 
        for (beam_ind = 0; beam_ind < self->max_beams; beam_ind++){
          if(error || obs_mask[beam_ind]){
            log_p += log(self->temp_obs[j][beam_ind]);
          }
        }
        
        sample->weight *= exp(log_p);
        
        total_weight += sample->weight;
      }      
  }
 
  delete [] obs_count; 
  delete [] obs_mask;
  return(total_weight);
}
 
void AMCLLaser::reallocTempData(int new_max_samples, int new_max_obs){
  if(temp_obs){
    for(int k=0; k < max_samples; k++){
      delete [] temp_obs[k];
    }
    delete []temp_obs; 
  }
  max_obs = new_max_obs; 
  max_samples = fmax(max_samples, new_max_samples); 
 
  temp_obs = new double*[max_samples]();
  for(int k=0; k < max_samples; k++){
    temp_obs[k] = new double[max_obs]();
  }
}