  Space exploration has witnessed revolutionary changes upon landing of the
Perseverance Rover on the Martian surface and demonstrating the first flight
beyond Earth by the Mars helicopter, Ingenuity. During their mission on Mars,
Perseverance Rover and Ingenuity collaboratively explore the Martian surface,
where Ingenuity scouts terrain information for rover's safe traversability.
Hence, determining the relative poses between both the platforms is of
paramount importance for the success of this mission. Driven by this necessity,
this work proposes a robust relative localization system based on a fusion of
neuromorphic vision-based measurements (NVBMs) and inertial measurements. The
emergence of neuromorphic vision triggered a paradigm shift in the computer
vision community, due to its unique working principle delineated with
asynchronous events triggered by variations of light intensities occurring in
the scene. This implies that observations cannot be acquired in static scenes
due to illumination invariance. To circumvent this limitation, high frequency
active landmarks are inserted in the scene to guarantee consistent event
firing. These landmarks are adopted as salient features to facilitate relative
localization. A novel event-based landmark identification algorithm using
Gaussian Mixture Models (GMM) is developed for matching the landmarks
correspondences formulating our NVBMs. The NVBMs are fused with inertial
measurements in proposed state estimators, landmark tracking Kalman filter
(LTKF) and translation decoupled Kalman filter (TDKF) for landmark tracking and
relative localization, respectively. The proposed system was tested in a
variety of experiments and has outperformed state-of-the-art approaches in
accuracy and range.

Computer Vision and Pattern Recognition