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jackal.yaml
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jackal.yaml
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# Input topics
robot_state_topic: "/wild_visual_navigation_node/robot_state"
desired_twist_topic: "/wild_visual_navigation_node/reference_twist"
# Relevant frames
fixed_frame: odom
base_frame: base_link
footprint_frame: base_link
# Robot size used to project the footprint
robot_length: 0.5
robot_width: 0.3
robot_height: 0.3
# Traversability estimation parameters
traversability_radius: 3.0 # Maximum distance in meters of the supervision graph
image_graph_dist_thr: 0.2 # Minimum distance between mission nodes being added
supervision_graph_dist_thr: 0.1 # Minimum distance between supervision nodes being added
network_input_image_height: 224 # Unput image height
network_input_image_width: 224 # Input image width
segmentation_type: "stego" # Options: slic, grid, random, stego
feature_type: "stego" # Options: dino, dinov2, stego
dino_patch_size: 8 # Options: 8, 16; We found 8 is sufficient and faster
dino_backbone: vit_small # Options: vit_small
slic_num_components: 100 # Number of segments for slic
confidence_std_factor: 1.0 # Tuning parameter to change the confidence computation / explained in confidence_generator.py
min_samples_for_training: 5 # Minimum number of mission nodes with successfull reprojection before start training
prediction_per_pixel: True # If true, trained network is inferenced for each pixel, otherwise per segment
vis_node_index: 10 # Defines node which is used for visualization, can help to debug the projected footprint
# 1 -> indicates latest added mission node; 10 -> indicates the node added 10 steps in the past
# Supervision Generator
untraversable_thr: 0.0 # Threshold for untraversable area -> this threshold is used to spawn special "collision walls" -> not used in the paper
mission_name: "test" # Name of the mission_folder used for logging
mission_timestamp: True # If true current timestamp is used as a prefix for the mission_folder
# Threads
image_callback_rate: 10 # Maximum rate at which images are processed in hertz
supervision_callback_rate: 10 # Maximum rate at which supervision_signals are processed in hertz
learning_thread_rate: 10 # Gradient steps per second in hertz
logging_thread_rate: 2 # Logging of learning_node in hertz
status_thread_rate: 0.5 # Status of feature_extractor_node in hertz
load_save_checkpoint_rate: 1.0 # Rate at which checkpoints are saved / loaded between learning and feature extraction
# Runtime options
device: "cuda"
mode: "online" # Options: "online", "debug", "extract_labels"; Sets operation mode of both nodes
colormap: "RdYlBu"
print_image_callback_time: false
print_supervision_callback_time: false
log_time: false
log_confidence: false
verbose: true
extraction_store_folder: "nan"