Evaluation of Local Feature Detectors for the Comparison of Thermal and Visual Low Altitude Aerial Images

Local features are key regions of an image suitable for applications such as image matching, and fusion. Detection of targets under varying atmospheric conditions, via aerial images is a typical defence application where multi spectral correlation is essential. Focuses on local features for the comparison of thermal and visual aerial images in this study. The state of the art differential and intensity comparison based features are evaluated over the dataset. An improved affine invariant feature is proposed with a new saliency measure. The performances of the existing and the proposed features are measured with a ground truth transformation estimated for each of the image pairs. Among the state of the art local features, Speeded Up Robust Feature exhibited the highest average repeatability of 57 per cent. The proposed detector produces features with average repeatability of 64 per cent. Future works include design of techniques for retrieval of corresponding regions

SWIR Camera-Based Localization and Mapping in Challenging Environments

International audienceThis paper assesses a monocular localization system for complex scenes. The system is carried by a moving agent in a complex environment (smoke, darkness, indoor-outdoor transitions). We show howusing a short-wave infrared camera (SWIR) with a potential lightingsource is a good compromise that allows to make just a slight adaptationof classical simultaneous localization and mapping (SLAM) techniques.This choice made it possible to obtain relevant features from SWIR images and also to limit tracking failures due to the lack of key points insuch challenging environments. In addition, we propose a tracking failure recovery strategy in order to allow tracking re-initialization with orwithout the use of other sensors. Our localization system is validatedusing real datasets generated from a moving SWIR-camera in indoor environment. Obtained results are promising, and lead us to consider theintegration of our mono-SLAM in a complete localization chain includinga data fusion process from several sensors