@@ -10,7 +10,7 @@ The following image is a basic representation of the slam problem. Is a sequence

<imgsrc="doc/images/slam_problem_sketch.png"alt="Image: Slam problem overview">

When searching for new landmarks, there is a landmark time persistance filter that checks that a landmark is seen enough times on the same location before adding it as a mapped landmarks.

When searching for new landmarks, there is a landmark time persistance filter that checks that a landmark is seen enough times on the same location before adding it as a mapped landmarks. An orientation filter can be enabled to check that it has a similar orientation on each detection.

To match a detection with a landmark the [Mahalanobis distance](https://en.wikipedia.org/wiki/Mahalanobis_distance) is used. Basically, a match between a detection and a landmark is done when the detection is inside an ellipse centered on the landmark. The ellipse is defined by the sensor noise and the mahalanobis distance parameter.

@@ -4,20 +4,20 @@ On this document there are some guidelines to adjust some important parameters.

# Adjust landmarks matching

If you realize that some detections are close to a landmark but is not matched with the landmark, you can adjust the following parameters:

***sensor_sigma_th** and **sensor_sigma_r**: These parameters define the basic ellipse for matching purposes. Bigger values on these parameters make a bigger ellipse. **WARNING:** Modifiying these parameters also affects to the covariance calculation and optimization.

***sensor_sigma_th** and **sensor_sigma_r**: These parameters define the basic ellipse for matching purposes. Bigger values on these parameters make a bigger ellipse. **WARNING:***Modifiying these parameters also affects to the covariance calculation and optimization.*

***landmark_mahalanobis_dist**: This parameters amplifies the ellipse.

# Adjust how much you trust on the camera information

You can modify the following parameters to adjust how much you trust on your ar detection information. **WARNING:** Modifiying these parameters also afects to the covariance calculation and optimization:

***sensor_sigma_th** and **sensor_sigma_r**: Bigger values on these parameters mean less trust on the laser information. **WARNING:** Modifiying these parameters also afects to the landmarks matching.

You can modify the following parameters to adjust how much you trust on your ar detection information. **WARNING:***Modifiying these parameters also afects to the covariance calculation and optimization:*

***sensor_sigma_th** and **sensor_sigma_r**: Bigger values on these parameters mean less trust on the laser information. **WARNING:***Modifiying these parameters also afects to the landmarks matching.*

# Adjust how much you trust on the odometry information

You can modify the following parameters to adjust how much you trust on your odometry information. **WARNING:** Modifiying these parameters also afects to the covariance calculation and optimization:

You can modify the following parameters to adjust how much you trust on your odometry information. **WARNING:***Modifiying these parameters also afects to the covariance calculation and optimization:*

***odom_fxy**, **odom_fth**, **odom_fxyth** and **odom_sigma_min**: Bigger values on these parameters mean less trust on the odometry information.

# Adjust how much you trust on the landmark localization

To modify the covariance calculation you can adjust the sensor sigma parameters and the odom noise parameters. Bigger values on these parameters mean less trust on the localization. **WARNING:** Modifying sensor sigmas also afects to the landmarks matching.

To modify the covariance calculation you can adjust the *sensor sigma parameters* and the *odom noise parameters*. Bigger values on these parameters mean less trust on the localization. **WARNING:***Modifying sensor sigmas also afects to the landmarks matching.*

# Adjust how much you trust on the amcl localization

You can modify the following parameters to adjust how much you trust on your amcl localization. **WARNING:** Modifiying these parameters also afects to the covariance calculation and optimization:

You can modify the following parameters to adjust how much you trust on your amcl localization when used by this node (normally when mapping landmarks or updating the map). **WARNING:***Modifiying these parameters also afects to the covariance calculation and optimization:*

***amcl_pose_estimated_sigma**: Bigger values on these parameters mean less trust on the amcl localization.