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VNwithSMap

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.

Semantic Map Visualization

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.

image

Robot Pose Visualiaztion

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Network

image

Preprocess

Add patches to House3D dataset

  • Add functions in preprocess/patch_core.py to the House3D/House3D/core.py file under the Environment class.

  • Add functions in preprocess/patch_house.py to the House3D/House3D/house.py file under the House class.

  • Integrate the colormap.csv to the House class by adding line 7 to 13 to House Class. This also generates self.smap and self.smap_img to the Class.

Parsing necessary data

  • preprocess/genhouseinfo.py generates the necessary house information for local semantic map as well. Modify the HOUSEDIR, CONFIGFILEPATH and house_ids. run by python preprocess/genhouseinfo.py.

  • preprocess/gensmap.py provides with a gensmap class and generates the local semantic map of 94 classes at every location given in a map.txt file previously generated (not included in the repo). change the house_ids and lmapszs to desired value and run by python preprocess/gensmap.py.

  • get_tar_star_minsteps_aseq.py generates action sequency for every location in given map.txt file for each given target.

Postprocess

Visualize the trajectory and learnt reward map and value map

  • res_vis.py gives a solution to generate all frames of learnt reward map and value map and local semantic map along the trajctory.

Training & Testing

Interactor

  • src/navi_env.py interacts with the aforementioned generated data.

  • src/multienv.py builds upon nav_env.py and interacts with multiple environments and enable multiple agents approaching different target (one agent one target).

Train & Test

  • tf_code/nav_agent_release.py initialize training or testing.

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Visual Navigation in indoor environment with Top-down Semantic Map with both supervised training and Deep Reinforcement Learning manner

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