{"action":"created","issue":{"url":"https://api.github.com/repos/floatlazer/semantic_slam/issues/4","repository_url":"https://api.github.com/repos/floatlazer/semantic_slam","labels_url":"https://api.github.com/repos/floatlazer/semantic_slam/issues/4/labels{/name}","comments_url":"https://api.github.com/repos/floatlazer/semantic_slam/issues/4/comments","events_url":"https://api.github.com/repos/floatlazer/semantic_slam/issues/4/events","html_url":"https://github.com/floatlazer/semantic_slam/issues/4","id":362386916,"node_id":"MDU6SXNzdWUzNjIzODY5MTY=","number":4,"title":"Source of world-to-camera tf?","user":{"login":"mfe7","id":3514533,"node_id":"MDQ6VXNlcjM1MTQ1MzM=","avatar_url":"https://avatars2.githubusercontent.com/u/3514533?v=4","gravatar_id":"","url":"https://api.github.com/users/mfe7","html_url":"https://github.com/mfe7","followers_url":"https://api.github.com/users/mfe7/followers","following_url":"https://api.github.com/users/mfe7/following{/other_user}","gists_url":"https://api.github.com/users/mfe7/gists{/gist_id}","starred_url":"https://api.github.com/users/mfe7/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/mfe7/subscriptions","organizations_url":"https://api.github.com/users/mfe7/orgs","repos_url":"https://api.github.com/users/mfe7/repos","events_url":"https://api.github.com/users/mfe7/events{/privacy}","received_events_url":"https://api.github.com/users/mfe7/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":1,"created_at":"2018-09-20T22:12:01Z","updated_at":"2018-10-23T18:49:03Z","closed_at":null,"author_association":"NONE","body":"Hi,\r\n\r\nCool package! I was able to get it installed in a docker image to isolate the installation from the rest of my system - I can share the dockerfile if you'd like.\r\n\r\nThe demo.bag file provided works quite well - the chair in the `/octomap_full` is roughly the shape of a chair, for example.\r\n\r\nBut when I test on data I collected, the SLAM part of the code doesn't seem to be working correctly, since one object is getting spread all over my octomap as it moves around in the camera plane. The code requires me to publish a world-to-camera tf in order to visualize the octomap in rviz. This tf was part of the demo.bag - where did it come from? Is there some external odometry source? Is that being used for the calculation of the octomap as well, or just display? I would expect the SLAM to find correspondences to reduce the odometry's accumulating error.\r\n\r\nI am also wondering if certain camera motions should be avoided, since in your demo the camera motion seems constrained - any tips?\r\n\r\nA video can be found [at this dropbox link](https://www.dropbox.com/s/tzlshiqae59vlz9/hummer.mp4?dl=0). The camera/depth is coming from a gazebo simulation, by the way.\r\n\r\nThanks!"},"comment":{"url":"https://api.github.com/repos/floatlazer/semantic_slam/issues/comments/432372955","html_url":"https://github.com/floatlazer/semantic_slam/issues/4#issuecomment-432372955","issue_url":"https://api.github.com/repos/floatlazer/semantic_slam/issues/4","id":432372955,"node_id":"MDEyOklzc3VlQ29tbWVudDQzMjM3Mjk1NQ==","user":{"login":"mfe7","id":3514533,"node_id":"MDQ6VXNlcjM1MTQ1MzM=","avatar_url":"https://avatars2.githubusercontent.com/u/3514533?v=4","gravatar_id":"","url":"https://api.github.com/users/mfe7","html_url":"https://github.com/mfe7","followers_url":"https://api.github.com/users/mfe7/followers","following_url":"https://api.github.com/users/mfe7/following{/other_user}","gists_url":"https://api.github.com/users/mfe7/gists{/gist_id}","starred_url":"https://api.github.com/users/mfe7/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/mfe7/subscriptions","organizations_url":"https://api.github.com/users/mfe7/orgs","repos_url":"https://api.github.com/users/mfe7/repos","events_url":"https://api.github.com/users/mfe7/events{/privacy}","received_events_url":"https://api.github.com/users/mfe7/received_events","type":"User","site_admin":false},"created_at":"2018-10-23T18:49:03Z","updated_at":"2018-10-23T18:49:03Z","author_association":"NONE","body":"Thanks for the clarification. I was able to get good performance using the robot's wheel encoders + IMU as an odometry source, without needing to pre-map the environment or take much care to initialize the solution.\r\n\r\nI was able to make a really accurate RGB octomap using `tree_type=0`, which I believe ignores the semantic part of the data. I will probably need to train an image segmentation model that's more suited to my domain, since I believe the inaccuracy in segmentation leads to a poor octomap.\r\n\r\nIn the meantime, it'd be awesome to create the octomap's geometry using just the RGB values, and then fill in the color of voxels with their semantic value instead of RGB value. That way I can be sure the octomap's geometry will be correct, even if some of the colors aren't fully accurate due to segmentation errors. Is that idea supported by this project? Any suggestions how I might do it?\r\n\r\nRegarding the docker image, it's pretty messy since there was a bug in one of the ros-comm binaries, so I built ros from source in the dockerfile. If I can clean it up I will open a PR."}}
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