Large-Scale SLAM?
Hello, all,
I have been testing various SLAM packages in ROS. I am just wondering is there any "large-scale" SLAM packages available? By large-scale I mean the map produced can be as large as a few kilometer^2 and the occupany grid map can be overlaid on a GIS software. Currently it seems to me that all packages can only produce a relatively small map, for example, 50 meters x 50 meters. I understand that large map construction will increase memory and cpu usage. So, is there any efficient way to do that?
What kinds of features are you mapping?
I am using 2D LiDAR to construct the map. The map is just a 2D occupancy grid.
I would be curious to see your findings on those existing algorithms and what you end up using. :)
I tried gmapping and hector slam, it seems that the map size is limited (authors please correct me if I am wrong).
I believe hector slam does use a fixed map size, but I think gmapping should expand to an arbitrary size. My gmapping setup uses an expanding map for sure. Whether there is a hard coaxed maximum or you run into memory or computational limitations is another matter however. :-)
Since a robot is only interested to the region nearby most of the time, it makes sense that the map should "fade away" when the robot moves to different locations. The question is, what is the best to construct, store and query a large-scale map which consists of many smaller maps? I heard of map stitch, but it is a tool rather than a complete solution.
Something like this? http://www.youtube.com/watch?v=sp49CQ_wYiI