  Active visual SLAM finds a wide array of applications in GNSS-Denied
sub-terrain environments and outdoor environments for ground robots. To achieve
robust localization and mapping accuracy, it is imperative to incorporate the
perception considerations in the goal selection and path planning towards the
goal during an exploration mission. Through this work, we propose FIT-SLAM
(Fisher Information and Traversability estimation-based Active SLAM), a new
exploration method tailored for unmanned ground vehicles (UGVs) to explore 3D
environments. This approach is devised with the dual objectives of sustaining
an efficient exploration rate while optimizing SLAM accuracy. Initially, an
estimation of a global traversability map is conducted, which accounts for the
environmental constraints pertaining to traversability. Subsequently, we
propose a goal candidate selection approach along with a path planning method
towards this goal that takes into account the information provided by the
landmarks used by the SLAM backend to achieve robust localization and
successful path execution . The entire algorithm is tested and evaluated first
in a simulated 3D world, followed by a real-world environment and is compared
to pre-existing exploration methods. The results obtained during this
evaluation demonstrate a significant increase in the exploration rate while
effectively minimizing the localization covariance.
