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Simplest (but with a bit of duplicated effort): create a std::list or std::vector that holds incoming samples (ie: PointClouds). Then every so often (ie: after N new samples), run your averaging algorithm.
To make it nice: add a sliding window approach (ie: throw away/pop the M last samples after each averaging run).
With a bit more reuse: make use of the message_filters package. For a simple cache, you could look at the Cache filter.
| | 2 | No.2 Revision |
Edit: see #q261816 for a duplicate of your question, but with an answer in Python.
Simplest (but with a bit of duplicated effort): create a std::list or std::vector that holds incoming samples (ie: PointClouds). Then every so often (ie: after N new samples), run your averaging algorithm.
To make it nice: add a sliding window approach (ie: throw away/pop the M last samples after each averaging run).
With a bit more reuse: make use of the message_filters package. For a simple cache, you could look at the Cache filter.
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