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There are two open source options available that I know of and that should work 'out of the box' after setup of the system (robot model, tf frames etc.) :

• The mapping system developed within the CityFlyer project from CCNY uses a scanmatching approach for generating a odometry estimate and feeds this to gmapping. Described here.

• hector_slam uses fast scan alignment at 40Hz directly and does not require odometry data. Both available tutorials should help with robot setup. Described in this paper. (Note I'm involved here, shameless plug and all :) )

Both approaches have been tested successfully with datasets recorded on quadrotors.

There are two open source options available that I know of and that should work 'out of the box' after setup of the system (robot model, tf frames etc.) :

• The mapping system developed within the CityFlyer project from CCNY uses a scanmatching approach for generating a odometry estimate and feeds this to gmapping. Described here.

• hector_slam uses fast scan alignment at 40Hz directly and does not require odometry data. Both available tutorials should help with robot setup. Described in this paper. (Note I'm involved here, shameless plug and all :) )

Both approaches have been tested successfully with datasets recorded on quadrotors.

There are two open source options available that I know of and that should work 'out of the box' after setup of the system (robot model, tf frames etc.) :

• The mapping system developed within the CityFlyer project from CCNY uses a scanmatching approach for generating a odometry estimate and feeds this to gmapping. Described here.

• hector_slam uses fast scan alignment at 40Hz directly and does not require odometry data. Both available tutorials should help with robot setup. Described in this paper. (Note I'm involved here, shameless plug and all :) )

Both approaches have been tested successfully with datasets recorded on quadrotors.