Abstract:
In the age of self-driving, LIDAR and IMU represent two of the most ubiqui-
tous sensors in use. Kalman Filtering and loosely coupled approaches dominate
industry techniques, while current research trends towards a more tightly coupled
formulation involving a joint optimization of IMU and LIDAR measurements.
After two years of experience working with and creating tightly coupled LIO
systems for offroad and indoor environments, we detail our findings regarding
such tightly coupled implementations. Moreover, we present a general frame-
work involving point-to-point based registration, an adaptive robust kernel, and
state-of-the-art preintegration for odometry. Our method operates in real-time
on a moderately powerful CPU, and we showcase its capabilities in high speed
off road environments and indoor environments.

Committee:
Michael Kaess (advisor)
Zach Manchester
Shibo Zhao