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 1 | # Towards Long Term SLAM on Thermal Imagery
 2 | This is the repository for the thermal imagery dataset presented in ["Towards Long Term SLAM on Thermal Imagery"](https://arxiv.org/abs/2403.19885) submitted to IROS 2024. The goal of the dataset is to provide training data, and to provide a baseline for testing day to night, and night to day relocalization on IR imagery for SLAM. The dataset format is described below. In the paper we use a fork of DBoW2 which can be found [here](https://github.com/c-keil/DBoW2), and a fork of Gluestick which can be found [here](https://github.com/c-keil/GlueStick). We are not realeasing the SLAM code at this time due to some overlap with currently unpublished work.
 3 | 
 4 | The dataset is currently hosted [HERE](https://drive.google.com/drive/folders/1icfFiF5_Dr6Gu2h0CgKb51CQRqeyWsMX?usp=drive_link).
 5 | 
 6 | ## Dataset Summary
 7 | The dataset has three main parts:
 8 | 1. 24 hour timelapses of static scenes
 9 | 2. Paired day-night stereo trajectories collected in the same location with an RTK GPS position ground truth
10 | 3. Simple thermal image sequences that can be used for training BoW vocabularies, or other unsupervised SLAM tasks
11 | All imagery is presented in a 640x512 16bit raw format collected with FLIR Boson ADK cameras with a 75 degree horizontal field of view.
12 | 
13 | ## Timelapse Sets
14 | 
15 | ## Paired Trajectories
16 | 
17 | ## Unsupervised Training Data
18 | 
19 | ### CLAHE
20 | In our work we preprocess images with the Matlab implementation of CLAHE with the following settings:
21 | ```
22 | adapthisteq(im(:,:,1), 'clipLimit', 0.4, 'NBins', 2^16, 'Distribution', 'rayleigh');
23 | ```
24 | 


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