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.
-
Add functions in
preprocess/patch_core.py
to theHouse3D/House3D/core.py
file under theEnvironment
class. -
Add functions in
preprocess/patch_house.py
to theHouse3D/House3D/house.py
file under theHouse
class. -
Integrate the
colormap.csv
to theHouse
class by adding line 7 to 13 toHouse
Class. This also generatesself.smap
andself.smap_img
to the Class.
-
preprocess/genhouseinfo.py
generates the necessary house information for local semantic map as well. Modify theHOUSEDIR
,CONFIGFILEPATH
andhouse_ids
. run bypython preprocess/genhouseinfo.py
. -
preprocess/gensmap.py
provides with agensmap
class and generates the local semantic map of 94 classes at every location given in amap.txt
file previously generated (not included in the repo). change thehouse_ids
andlmapszs
to desired value and run bypython preprocess/gensmap.py
. -
get_tar_star_minsteps_aseq.py
generates action sequency for every location in givenmap.txt
file for each given target.
res_vis.py
gives a solution to generate all frames of learnt reward map and value map and local semantic map along the trajctory.
-
src/navi_env.py
interacts with the aforementioned generated data. -
src/multienv.py
builds uponnav_env.py
and interacts with multiple environments and enable multiple agents approaching different target (one agent one target).
tf_code/nav_agent_release.py
initialize training or testing.