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T1c-map - Map building

The main task is to implement a function that will fuse the laser scan data into the occupancy grid map.

Deadline 12. October 2019, 23:59 PST
Points 2
Label in BRUTE t1c-map
Files to submit archive with GridMapFuser.py file
Resources T1c-map resource package
Blocks V-REP scene


In file GridMapFuser.py implement the fuse_laser_scan function. The purpose of the function is to fuse new data into the occupancy grid map as the robot traverses the environment. The occupancy grid map is initialized to the size of the VREP scene (10$\times$10 m). The laser scan measurements shall be fused to the grid using the Bayesian update described in Lab03 - Grid-based Path Planning.

The obstacle sensing is achieved using the simulated laser range finder through the RobotHAL interface through the self.robot object.

scan_x, scan_y = self.robot.get_laser_scan()
The laser scan is in relative coordinates w.r.t. the hexapod base where the $x$ axis corresponds to the heading of the robot and $y$ axis is perpendicular to the robot heading.

The fuse_laser_scan function has a following prescription

def fuse_laser_scan(gridmap, pose, scan_x, scan_y):
    Method to fuse the laser scanner data into the map
    gridmap: (GridMap.GridMap)
        gridmap where to fuse the laser scans
    pose: (float, float, float)
        pose of the robot (x, y, orientation)
    scan_x: list(float)
    scan_y: list(float)
        relative x and y coordinates of the points in laser scan w.r.t. the current robot pose


The recommended approach for the occupancy map building using Bayesian approach is described in Lab03 - Grid-based Path Planning.

The GridMap represents a probabilistic representation of the word. In particular the variable “GridMap.grid” holds the probabilities of individual states to be occupied GridMap.grid['p'] and the derived binary information about the passability of the given cell GridMap.grid['free']. Access and update of the probabilities can be done using functions GridMap.get_cell_p(coord) and GridMap.set_cell_p(coord) that will also automatically update the passability information, when the probability changes.

Hence the correct approach to the fusion of the laser scan data into the occupancy grid map is as follows

  1. Compensate for the heading of the robot by rotating the scanned points
  2. Compensate for the position of the robot by offsetting the scanned points to the robot's coordinates
  3. Raytrace individual scanned points (preferably using Bresenham line algorithm, e.g., bresenham_line(start, goal)) which will give you coordinates of the cells which occupancy probability should be updated
  4. Update the occupancy grid using the Bayesian update and the simplified laser scan sensor model with $\epsilon = 0$, i.e., using the Bresenham line algorithm trace all the points lying between the position of the robot and the reflection point $(d \in [0,r))$ and update their probability (being free) and only the reflection point $(d = r)$ being occupied
Note, when using the sensory model as it is described in Lab03 - Grid-based Path Planning, once the grid cell probability is set to 0, it stays 0, hence, all the nice features of the probabilistic mapping vanishes. Therefore it is recommended to use a different sensory model with: $$ S^z_{occupied} = \begin{cases} 0.95 \qquad\text{for}\qquad d = r\\ 0 \qquad\text{otherwise} \end{cases}, $$ $$ S^z_{free} = \begin{cases} 0.95 \qquad\text{for}\qquad d \in [0,r)\\ 0 \qquad\text{otherwise} \end{cases}, $$

Laser scan dataset creation

To test the fuse_laser_scan function, it is recommended to construct an evaluation dataset by recording the data necessary as the input of the function during a single simulated run and then only read a process these data. To run the recording script t1c-map-collect.py copy the robot folder to facilitate the robot locomotion. The recording script is as follows

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import sys
import math
import time
import numpy as np
import threading
import Robot as rob
def collect_scans(robot, filename):
    with open(filename,'w') as scanfile:
        print ( 'Started collecting' )
        while not robot.navigation_stop:
            scan = None
            pose = None
            # read scan
            while scan is None or len(scan[0]) < 1 or pose is None or np.any(np.isnan(pose)) or np.any(np.isnan(scan)):
                pose = robot.get_pose()
                scan = robot.robot.get_laser_scan()
            scan_x, scan_y = scan
            # write scan to file
            towrite = '%f,%f,%f,%s,%s\n' % (pose[0],pose[1],pose[2],','.join(['%f' % scanval for scanval in scan_x]),','.join(['%f' % scanval for scanval in scan_y]))
        print ('Scanning done')    
if __name__=="__main__":
    #navigation points
    route = [(8.5, 4.0, None), (6.0, 6.0, None), (5.0, 5.0, None), (1.5, 8.5, None)]
    if len(sys.argv) >= 2:
        scanfile = sys.argv[1]
        scanfile = 'test.scan'
    print ( 'Collecting scan into file %s' % scanfile)    
    #instantiate the robot
    robot = rob.Robot()
    robot.navigation_stop = False
    #run scan collection
    scan_thread = threading.Thread(target=collect_scans, args=(robot,scanfile))
    for waypoint in route:
        pos_des = waypoint[0:2]
        print("Navigation point " + str(pos_des))
        #navigate the robot towards the target
        status1 = robot.goto(pos_des)
    #stop the robot
    robot.navigation_stop = True                      


Using a dataset collected by the recording script, the code can be evaluated using the following script (also attached as t1c-map-eval.py)

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import sys
import os
import math
import time
import numpy as np
import matplotlib.pyplot as plt
import GridMap as gmap
import GridMapFuser as fuser
if __name__ == "__main__":
    if len(sys.argv) >= 2:
        scanfile = sys.argv[1]
        scanfile = 'test.scan'
    gridmap = gmap.GridMap(100, 100, 0.1)
    i = 0
    with open(scanfile,'r') as scanfile:
        for scanline in scanfile:
            vals = scanline.replace('\n','').split(',')
            pose = [ float(val) for val in  vals[:3]]
            d = int ( ( len(vals) - 3 ) / 2 )
            scan_x = [ float(val) for val in  vals[3:3+d] ]
            scan_y = [ float(val) for val in  vals[3+d:] ]
            i += 1
            print ('Fusing scan #%d' % i)
            fuser.fuse_laser_scan(gridmap, pose, scan_x, scan_y)
            #plot the gridmap
            if FLAG_PLOT:
    if FLAG_PLOT:

Possible built maps

Following figure visualize the possible sequence of map building given the evaluation script.

courses/b4m36uir/hw/t1c-map.txt · Last modified: 2019/10/06 01:47 by pragrmi1