POLAR-Sim: Augmenting NASA's POLAR dataset for data-driven lunar perception and rover simulation

NASA's POLAR (Polar Optical Lunar Analog Reconstruction) dataset contains approximately 2,600 pairs of high dynamic range stereo photos captured across 12 varied terrain scenes, including areas with sparse or dense rock distributions, craters, and rocks of different sizes. The purpose of these photos is to spur research and development in robotics, AI-based perception, and autonomous navigation. Acknowledging a scarcity of lunar photos from around the lunar poles, NASA Ames produced on Earth but in controlled conditions, photos that resemble rover operating conditions from these regions of the Moon.
 
This dataset, named POLAR-Sim, provides bounding boxes and semantic segmentation information for all the photos in NASA's POLAR dataset. This effort results in 23,000 labels and semantic segmentation information pertaining to rocks and shadows of rocks. Furthermore, for each scene, we produced individual meshes associated with the ground and the rocks in each scene. This allows anyon...,
Photo Bounding Box Annotation

To support the training of data-driven perception algorithms, we manually labeled bounding boxes for all of the rocks and rocks' shadows in the POLAR dataset. This effort was motivated by the observation that object detection for rocks and shadows plays an important role in autonomous navigation -- large rocks can block the rover's path, while medium and small rocks can damage the wheels or the chassis. Shadows also help estimate the Sun's position, which is vital for navigation planning, solar energy harvesting, and sensor orientation.

Approximately 23,000 rocks and rocks' shadows were labeled. Each photo's configuration includes the terrain ID, stereo camera position (A: 1.5 m from terrain center at 0 deg, B: 4 m from terrain center at 0 deg, or C: 1.5 m from terrain center at 280 deg), rover light status (ON or OFF), Sun azimuth angle (none, 30, 180, 270, or 350 degrees), stereo camera index (Left or Right), and exposure time (32 to 2048 ms), where \"n..., # POLAR-Sim

[https://doi.org/10.5061/dryad.ksn02v7hf](https://doi.org/10.5061/dryad.ksn02v7hf)

A database of bounding box and semantic segmentation labels and terrain meshes for the [POLAR dataset](https://ti.arc.nasa.gov/dataset/IRG_PolarDB/)

## Cover photos of rock indices

Pictures in the *CoverPhotoOfIndices.zip* folder show how we index the rocks in each terrain. The indices do not meet the label orders in the bounding box label txt files. The indices meet the rock ID of the mesh files in each terrain. Original pictures come from the POLAR dataset.

# Semantic segmentation labels

Please check the *SegmentLabels_Terrain[terrain ID].zip* folders. The annotations were done with Roboflow. The semantic segmentation label files in YOLO format are categorized in the terrain ID folders. Each txt file corresponds with one HDR photo of the POLAR dataset.

* The label files are named as the following rule:
\[terrain ID] \_ [stereo camera position] \_ [rover light on/off] \_ [Sun azimuth...,