code for paper 'Learning View and Target Invariant Visual Servoing for Navigation'

Setup GibsonEnv

Download and install gibson environment from https://github.com/StanfordVL/GibsonEnv.

git checkout d3aa0a1

Generate Training and Testing data

python sample_image_pairs_with_common_area.py --scene_idx=0
python visualize_sampled_image_pairs.py

Learned Visual Servoing Model

To train a learned-vs model through DQN,

python vs_controller/train_DQN_vs_overlap.py

To evaluate the performance of the trained model,

python vs_controller/evaluate_DQN_vs.py

Classical VS Model

Test the vs model using sift and ground-truth depth,

python visual_servoing/test_IBVS_SIFT_interMatrix_gtDepth_Vz_OmegaY.py  --scene_idx=$i

Generate Occupancy Map

Used some code from https://github.com/tenther/cs685-project.

To generate nice occupancy maps for the used Gibson environments,

sh rrt/run_make_rrt.sh