{"action":"closed","issue":{"url":"https://api.github.com/repos/chicagoedt/revo_robot/issues/86","labels_url":"https://api.github.com/repos/chicagoedt/revo_robot/issues/86/labels{/name}","comments_url":"https://api.github.com/repos/chicagoedt/revo_robot/issues/86/comments","events_url":"https://api.github.com/repos/chicagoedt/revo_robot/issues/86/events","html_url":"https://github.com/chicagoedt/revo_robot/issues/86","id":108674056,"number":86,"title":"Use Stereo SLAM for wheel odom rotational covariance","user":{"login":"l0g1x","id":5489842,"avatar_url":"https://avatars.githubusercontent.com/u/5489842?v=3","gravatar_id":"","url":"https://api.github.com/users/l0g1x","html_url":"https://github.com/l0g1x","followers_url":"https://api.github.com/users/l0g1x/followers","following_url":"https://api.github.com/users/l0g1x/following{/other_user}","gists_url":"https://api.github.com/users/l0g1x/gists{/gist_id}","starred_url":"https://api.github.com/users/l0g1x/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/l0g1x/subscriptions","organizations_url":"https://api.github.com/users/l0g1x/orgs","repos_url":"https://api.github.com/users/l0g1x/repos","events_url":"https://api.github.com/users/l0g1x/events{/privacy}","received_events_url":"https://api.github.com/users/l0g1x/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"milestone":null,"comments":3,"created_at":"2015-09-28T14:51:36Z","updated_at":"2015-11-18T18:51:06Z","closed_at":"2015-11-18T18:51:06Z","body":"Ive been thinking for a while now on how we can estimate (better) the rotational position/velocity done by the robot, and i had a few ideas:\r\n -  Attach a aruco marker to the top of the robot and have a overhead camera that records the rotation made. We would use the RC remote to do a full 360 degree turn, and then compare the angular position data produced by the wheel odometry, to then be able to specify a more accurate rotational covariance.\r\n - Have some visual slam that produces some type of odom, and use that in the covariance calculation of wheel odometry. We would only use the rotational component of the visual slam *solely* for wheel odometry rotational covariance calculations. The visual slam odom values would be its own input. We would just use the visual odom as a rotational comparison to what we got on the rotational wheel odometry\r\n\r\nThoughts on this?"}}