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.

通过FIT-SLAM方法，该研究提出了一种新的3D环境探索方法，旨在提高无人地面车辆的探索速率并优化SLAM精度，该方法通过估计全局可通过性地图并结合SLAM后端使用的地标信息，实现了可靠的定位和路径规划。

基于費舍爾信息和可穿越性估計的主動SLAM方法用於三維環境探索