Good map with hector_mapping and with odometry-only - poor map with gmapping

asked 2015-02-01 10:32:36 -0500

Kristof
141 ●2 ●6 ●10

updated 2015-02-03 11:26:56 -0500

Using an XV11 lidar and calibrated odometry, I am able to build a good quality map using both hector_mapping (using scan results only) and pure odometry (using odometry only). However, I am unable to get a good map using gmapping (which combines scan results and odometry).

Odometry:

My robot has odometry that passes the tests listed in the navigation tuning guide - linear translation is perfect, rotation error seems to be reasonable as well.

Lidar:

Additionally, it is using an XV11 lidar as laser scanner, with the xv_11_laser_driver package.

Bagfile:

I've recorded a short bag file with /odom, /tf and /scan topics, using following command:

rosbag record -O second_data /scan /tf /odom

I then compressed the bagfile (rosbag compress second_data.bag), and put it available here.

Map results:

If I transform this bag file into a map using hector_mapping, the result looks perfect: image description

EDIT: Here are links to the hector_mapping generated pgm and yaml files.

Moreover, if I simply display the laserscan results in rviz in the /odom frame, with an infinite decay time, I get a very nice map as well: image description

Now, when I try to run this through gmapping, the map looks very poor: image description

EDIT: Here are links to the gmapping generated pgm and yaml files.

Commands:

To generate the gmapping map, I used:

rosparam set use_sim_time true
rosparam set slam_gmapping/delta 0.1  #set resolution to 0.1
rosrun gmapping slam_gmapping scan:=scan
rosbag play second_data.bag --clock
rosrun map_server map_saver

To generate the hector_mapping map, I used:

rosparam set use_sim_time true
roslaunch hector_mapping mapping_default.launch
rosbag play second_data.bag --clock
rosrun map_server map_saver

Launch file is available here.

PDF graph of tf tree is available here.

Question:

How come gmapping gives such poor results, compared to the two other approaches? Any suggestions to improve the gmapping results would be much appreciated.

Thank you,

Kristof

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Comments

I had a very similar experience with the xv-11 laser. I got the best results from gmapping by driving my robot in a straight line, super slow (around 0.02 m/s I think).

jseal ( 2015-02-06 23:48:19 -0500 )edit

@jseal interesting, thanks! Did you ever try setting the minimumScore parameter? Setting this to a very high value (see below) works very well for me.

Kristof ( 2015-02-07 03:34:41 -0500 )edit

I haven't, I'm not sure how I missed that one. I'm going to give it a try. I ended up stitching a couple of maps together with gimp to produce my final map.

jseal ( 2015-02-07 09:32:17 -0500 )edit

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answered 2015-02-06 04:44:46 -0500

Kristof
141 ●2 ●6 ●10

updated 2015-02-08 01:50:08 -0500

I did a lot of different gmapping experiments - results can be found here.

Summary of what I tried that did NOT help:

Decreasing linear_update and angular_update to 0.1 (from default 1.0) (Note: increasing to 2.0 works a little bit better, presumably because this means the laser scans have become more dissimilar before being processed)
Increasing particles to 100, 500

Summary of what did help:

I had most success with setting the minimumScore to a very high value, as suggested here, and combining that with setting srr/srt/str to 0, and stt to 0.1 (to reflect rotational error, but near-perfect translational behavior), i.e.:

rosrun gmapping slam_gmapping scan:=scan _delta:=0.1 _maxUrange:=4.99 _xmin:=-5.0 _ymin:=-5.0 _xmax:=5.0 _ymax:=5.0 _particles:=30 _srr:=0 _srt:=0 _str:=0 _stt:=0.1 _minimumScore:=10000

(Note that the maxUrange, and particles values are the defaults; the xmin/ymin/xmax/ymax just make for a smaller map, by specifying much lower starting values than the default 100m)

The result is this (pgm): image description

Note that results change across runs - sometimes results would be slightly worse. In particular, the "_srr:=0 _srt:=0 _str:=0 _stt:=0.1" seems not very significant - with all these set to 0, and particles set to 1, that is with:

rosrun gmapping slam_gmapping scan:=scan _delta:=0.1 _maxUrange:=4.99 _xmin:=-5.0 _ymin:=-5.0 _xmax:=5.0 _ymax:=5.0 _particles:=1 _srr:=0 _srt:=0 _str:=0 _stt:=0 _minimumScore:=10000

I get this (pgm):

image_description

Slightly different, but well within the variance I get by running the previous command.

Lastly, by simply using the defaults for srr/srt/str/stt, i.e.:

rosrun gmapping slam_gmapping scan:=scan _delta:=0.1 _maxUrange:=4.99 _xmin:=-5.0 _ymin:=-5.0 _xmax:=5.0 _ymax:=5.0 _minimumScore:=10000

I get this (pgm):

image_description

Again slightly different, but well in line with what I get by running the first command.

So in summary, changing minimumScore to a very large value solves my problem. (I tried 500, 750, 1000, 10 000, with 10 000 best)

So this seems to solve my issue. I am still open to better solutions though, as this seems a bit of a hack, disabling a large part of gmapping's 'intelligence'.

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The documentation for minimumScore ("Can avoid jumping pose estimates [...] when using laser scanners with limited range (e.g. 5m).") suggests this might be a common problem with xv11 lidars (5m range). Have other xv11 lidar users a similar experience with gmapping?

Kristof ( 2015-02-07 03:32:32 -0500 )edit

Hi @Kristof, it would be interesting to check for a slightly larger map the parameters you used. I think gmapping performs well when there are objects within the range of the sensor. For e.g.. in corridors it would be a different story, I will check by varying the minimumScore with my bagfile.

AlexR ( 2015-02-07 22:49:30 -0500 )edit

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answered 2015-02-05 07:18:26 -0500

AlexR
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I would suggest going through this answer. Gmapping results depends a lot on different parameters specially number of particles used, linear update rate and angular update rate. Tweaking these parameters in the gmapping.launch file can produce little better result.

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@AlexR, thanks I went through that answer, and in particular the reference to this answer seems promising - still testing, but will report back shortly.

Kristof ( 2015-02-06 03:01:55 -0500 )edit

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Good map with hector_mapping and with odometry-only - poor map with gmapping

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Good map with hector_mapping and with odometry-only - poor map with gmapping