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About

Kenny Chen is a robotics researcher whose work centers on autonomous navigation, state estimation, and LiDAR-based perception for mobile and aerial robots. He is best known for developing **Direct LiDAR Odometry (DLO)** and its inertial extension, **Direct LiDAR-Inertial Odometry (DLIO)**, two landmark contributions that have reshaped how resource-constrained robots achieve fast, accurate localization in demanding real-world environments. DLO introduced a lightweight frontend odometry pipeline capable of handling dense point clouds without sacrificing speed or consistency, earning over 205 citations since its 2022 publication and establishing itself as a widely adopted benchmark in the field. DLIO further advanced this work by addressing motion distortion through continuous-time correction, critical for agile aerial platforms and robots traversing rough terrain, accumulating 135 citations within a year of release. Beyond state estimation, Chen has contributed to autonomous exploration through adaptive coverage path planning and has played a role in the DARPA Subterranean Challenge as part of Team CoSTAR's NeBula autonomy framework, tackling large-scale underground environments. With over 350 cumulative citations, his research reflects a consistent focus on making robust autonomy practical for real-world deployment on computationally limited platforms.