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For the map to react more quickly to changes, the sensor_model parameters are indeed the right ones to tune. For a detailed description, please read the OctoMap paper available at http://octomap.sourceforge.net/ or an introduction on occupancy grid mapping.

Essentially, each endpoint increases the probability that a voxel is occupied by sensor_model/hit, each other voxel's probability along the ray to the endpoint is decreased by the probability of sensor_model/miss. The minimum and maximum probabilities are given by the thresholds sensor_model/min and sensor_model/max. All operations are performed in log-odds space, so the probabilities are added (hit) or subtracted (miss) until they reach min or max in log-space. If you increase the hit probability and decrease the miss probability, it will make the map trust the sensor more after one measurement. If you increase min and decrease max at the same time, this will enable fast updates at the cost of more sensor noise appearing in the map.

The latch parameter only affects the publishing of visualizations, if your map changes often best set it to false.