minor changes to smac_planner README

nav2_smac_planner/README.md

@@ -172,7 +172,7 @@ By default, `0.4m` is the setting which I think is "reasonable" for the smaller
 
 ### Costmap Resolutions
 
-We provide for the Hybrid-A\*, State Lattice, and 2D A\* implementations a costmap downsampler option. This can be **incredible** beneficial when planning very long paths in larger spaces. The motion models for SE2 planning and neighborhood search in 2D planning is proportional to the costmap resolution. By downsampling it, you can N^2 reduce the number of expansions required to achieve a particular goal. However, the lower the resolution, the larger small obstacles appear and you won't be able to get super close to obstacles. This is a trade-off to make and test. Some numbers I've seen are 2-4x drops in planning CPU time for a 2-3x downsample rate. For long and complex paths, I was able to get it << 100ms at only a 2x downsample rate from a plan that otherwise took upward of 400ms.
+We provide for the Hybrid-A\*, State Lattice, and 2D A\* implementations a costmap downsampler option. This can be **incredibly** beneficial when planning very long paths in larger spaces. The motion models for SE2 planning and neighborhood search in 2D planning is proportional to the costmap resolution. By downsampling it, you can N^2 reduce the number of expansions required to achieve a particular goal. However, the lower the resolution, the larger small obstacles appear and you won't be able to get super close to obstacles. This is a trade-off to make and test. Some numbers I've seen are 2-4x drops in planning CPU time for a 2-3x downsample rate. For long and complex paths, I was able to get it << 100ms at only a 2x downsample rate from a plan that otherwise took upward of 400ms.
 
 I recommend users using a 5cm resolution costmap and playing with the different values of downsampling rate until they achieve what they think is optimal performance (lowest number of expansions vs. necessity to achieve fine goal poses). Then, I would recommend to change the global costmap resolution to this new value. That way you don't own the compute of downsampling and maintaining a higher-resolution costmap that isn't used.
 
@@ -209,4 +209,4 @@ As such, it is recommended if you have sparse SLAM maps, gaps or holes in your m
 
 One interesting thing to note from the second figure is that you see a number of expansions in open space. This is due to travel / heuristic values being so similar, tuning values of the penalty weights can have a decent impact there. The defaults are set as a good middle ground between large open spaces and confined aisles (environment specific tuning could be done to reduce the number of expansions for a specific map, speeding up the planner). The planner actually runs substantially faster the more confined the areas of search / environments are -- but still plenty fast for even wide open areas!
 
-Sometimes visualizing the expansions is very useful to debug potential concerns (why does this goal take longer to compute, why can't I find a path, etc), should you on rare occasion run into an issue. You can enable the publication of the expansions on the `/expansions` topic for SmacHybrid with the parameter `viz_expansions: True`, beware that it should be used only for debug as it increases a lot the CPU usage.
+Sometimes visualizing the expansions is very useful to debug potential concerns (why does this goal take longer to compute, why can't I find a path, etc), should you on rare occasion run into an issue. You can enable the publication of the expansions on the `/expansions` topic for SmacHybrid with the parameter `debug_visualizations: True`, beware that it should be used only for debug as it increases a lot the CPU usage.