presentation is here
Visual Navigation in indoor environment with Top-down Semantic Map.
by taking advantage of value iteration network, the action policy network is based on the value map generated.
Each Semantic Map has a size of H X W X C where H and W stands for height and width, and C is the number of object categories. Each cell on Semantic Map has a vector of object occupancies.
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Add functions in
preprocess/patch_core.pyto theHouse3D/House3D/core.pyfile under theEnvironmentclass. -
Add functions in
preprocess/patch_house.pyto theHouse3D/House3D/house.pyfile under theHouseclass. -
Integrate the
colormap.csvto theHouseclass by adding line 7 to 13 toHouseClass. This also generatesself.smapandself.smap_imgto the Class.
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preprocess/genhouseinfo.pygenerates the necessary house information for local semantic map as well. Modify theHOUSEDIR,CONFIGFILEPATHandhouse_ids. run bypython preprocess/genhouseinfo.py. -
preprocess/gensmap.pyprovides with agensmapclass and generates the local semantic map of 94 classes at every location given in amap.txtfile previously generated (not included in the repo). change thehouse_idsandlmapszsto desired value and run bypython preprocess/gensmap.py. -
get_tar_star_minsteps_aseq.pygenerates action sequency for every location in givenmap.txtfile for each given target.
res_vis.pygives a solution to generate all frames of learnt reward map and value map and local semantic map along the trajctory.
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src/navi_env.pyinteracts with the aforementioned generated data. -
src/multienv.pybuilds uponnav_env.pyand interacts with multiple environments and enable multiple agents approaching different target (one agent one target).
tf_code/nav_agent_release.pyinitialize training or testing.


