Title: Design and Integration of Semantic Mapping System for Forest Fire Mitigation

Abstract:
Remote sensing technologies can provide an automated approach to monitor and analyze conditions in the forest environment over a period of time for forest maintenance and wildfire mitigation efforts. In particular, unmanned aerial vehicles (UAVs) are a promising remote sensing modality since they can traverse uneven terrain and provide on-demand high-resolution surveys of the environment with various sensors.

In this work, we present the mechanical design and system integration of a multi-sensing payload to harness such capabilities of UAVs and collect meaningful data in forest environments with the ultimate aim of localizing clusters of flammable vegetation. Our payload primarily consists of  LiDAR, visual, and IMU sensors. We use pose information from implementing keyframe-based SLAM on our payload to globally register semantically labeled point clouds. Since the pose updates from the SLAM system are sparse compared to the semantically labeled point clouds, we implement a relative frame interpolation method that uses keyframes as constraints to derive interpolated poses for registration. We demonstrate our approach to real-world data collected using our sensing payload on a UAV operating in forest environments.

Committee:
Prof. George Kantor (advisor)
Prof. Sebastian Scherer
Daniel McGann

Zoom Link: https://cmu.zoom.us/j/95291452912?pwd=cFNOYmVWUTdtaUVicFQyc0pxUm82UT09
Meeting ID: 952 9145 2912
Passcode: 282394