While the different sensor channels produce inputs to the filter algorithm, the arguments are bound to the model functions which are used for propagation and importance weighting. Finally all measurements prepared in a frontend are pushed to processing queues in the backend. More detailed information about features of the current state of the approach can be found in the related paper .
MuSe-MCL2D is an instance of MuSe-SMC in use. Currently, we support 2D laser range finders with standard map representations and sensor models. We also recently developed a sensor model which works fine with stereo camera data based on NDT maps. Additionally, we support sensor models from former papers .
The picture above shows the MuSe-MCL2D multi sensor particle filter localization in action, supported by two 2D laser range finders (light green points representing the front laser and the orange ones the rear laser) and a stereo camera (grey). While the laser range finder data is used with a 2D occupancy grid map, the stereo camera data is compared to a 3D NDT map (bluish boxes represent means of joint distributions). The red arrows in the image are the particles and represent possible robot poses.
|||Richard Hanten, Cornelia Schulz, and Andreas Zell. A Generic Monte Carlo Framework for Multi-Sensor Fusion and its Application to Localization. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, October 2018. (Under review).|
|||Richard Hanten, Sebastian Buck, Sebastian Otte, and Andreas Zell. Vector-amcl: Vector based adaptive monte carlo localization for indoor maps. In Intelligent Autonomous Systems (IAS), The 14th International Conference on, Shanghai, CN, July 2016. [ details ]|