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robot_localization as simple filter without fusion

asked 2015-12-27 10:35:41 -0500

codenotes gravatar image

Can robot_localization be used without fusing multiple sources, ie, as a simple filter for odometry alone? I do not need or want to fuse an IMU with odometry, rather I simply want to smooth out the existing odometry.

Although the use-case may seem odd, I have a motion-capture system that gives more-or-less a real-time transform for the robot. However the mocap reported transform jitters a bit, and rarely it will jump in and out of an outlier. I could address this with a moving-average filter (too slow) or another more traditional filter (some might argue Kalman may be overkill) but I'd prefer to use the robot_localization infrastructure. In the future I may have other sensors to fuse and it makes sense to architect this system around RL.

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Could you show me your configuration files? I have the same question. But without IMU, it doesn't work.Thanks.

Devin gravatar image Devin  ( 2016-02-29 11:49:21 -0500 )edit

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answered 2016-02-05 08:21:19 -0500

Tom Moore gravatar image

Yes, you can do that. Just make sure the covariance on the measurements is high enough to allow the smoothing to happen. If it's too small, the filter output will also be jittery.

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Thanks...very good to know. So simply by omitting the IMU in the configuration, it will do that? Great...because sometimes you just need a filter. I guess is just uses the measurement itself as the model.

codenotes gravatar image codenotes  ( 2016-02-06 08:16:44 -0500 )edit

Yep, there are no restrictions on the number of sensor inputs. It uses its own omnidirectional kinematic model, which is independent of the measurement sources. It predicts with the model, then corrects with your sensor data.

Tom Moore gravatar image Tom Moore  ( 2016-02-06 15:08:45 -0500 )edit

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Asked: 2015-12-27 10:35:41 -0500

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Last updated: Feb 05 '16