  Search and Rescue (SAR) missions in harsh and unstructured Sub-Terranean
(Sub-T) environments in the presence of aerosol particles have recently become
the main focus in the field of robotics. Aerosol particles such as smoke and
dust directly affect the performance of any mobile robotic platform due to
their reliance on their onboard perception systems for autonomous navigation
and localization in Global Navigation Satellite System (GNSS)-denied
environments. Although obstacle avoidance and object detection algorithms are
robust to the presence of noise to some degree, their performance directly
relies on the quality of captured data by onboard sensors such as Light
Detection And Ranging (LiDAR) and camera. Thus, this paper proposes a novel
modular agnostic filtration pipeline based on intensity and spatial information
such as local point density for removal of detected smoke particles from Point
Cloud (PCL) prior to its utilization for collision detection. Furthermore, the
efficacy of the proposed framework in the presence of smoke during multiple
frontier exploration missions is investigated while the experimental results
are presented to facilitate comparison with other methodologies and their
computational impact. This provides valuable insight to the research community
for better utilization of filtration schemes based on available computation
resources while considering the safe autonomous navigation of mobile robots.
