Ibraim's profile - activity

Although this is an old post maybe someone new comes looking for the same information. I have found references of doing this with the name of SLAM and DATMO. This means SLAM with detection and tracking of moving objects.

In this method the people are treated in no different way as any other moving object in the scene and probably this is a better approach as there are significant different objects in indoor and outdoor environments.

You can take a look at the following references:

• Section 8.4 of the book probabilitics robotics by Fox, Thrun and Burgard
• Markov localization in dynamic environments by Fox, Burgard and Thrun
• Literature review of SLAM and DATMO is a good starting point
• The work by Wang at the Robotics Institute CMU. His 2002, 2003 and thesis project are a valuable resource
• All other references in the work of Pancham specially Montesano, Ortega and Vu PhD thesis

Hope this help anyone who arrives here.

The best way I have found to get started with ROS was by the examples, specially those related to turtlebot_simulator: http://www.ros.org/wiki/turtlebot_simulator

Hi all,

I'm using amcl for localization in a mobile robot. Now succesfully I have it working on the robot. My idea is to use it with a laser scan and I will be modifying the readings so the information passed to the localization algorithm is somehow filtered.

What I want to do now is to evaluate the performance of one filter vs. the original data, is there any information that amcl output so I can do it?. I think I can use the spread of the particles, but seeing from the execution this doesn't always reflect the accuracy of the localization, as sometimes the variance is small but the robot is not in its right orientation.

Other way to ask the question would be: how can I compare odometry vs. laser base localization using amcl algorithm?

Thanks for your thoughts and ideas,

Ibraim