Compared to the standard ILQR approach, our proposed approach achieves a 30% and 50% reduction in cross track error in Warthog and Moose, respectively, by utilizing only 30 minutes of real-world driving data.
This paper proposes SemCal: an automatic, targetless, extrinsic calibration algorithm for a LiDAR and camera system using semantic information.
We propose an algorithm for automatic, targetless, extrinsic calibration of a LiDAR and camera system using semantic information.
Off-road image semantic segmentation is challenging due to the presence of uneven terrains, unstructured class boundaries, irregular features and strong textures.
The data was collected on the Rellis Campus of Texas A\&M University and presents challenges to existing algorithms related to class imbalance and environmental topography.
Ranked #1 on 3D Semantic Segmentation on RELLIS-3D Dataset
We use model predictive control (MPC) to deal with model imperfections and perform extensive experiments to evaluate the performance of the controller on human driven reference trajectories with vehicle speeds of 3m/s- 4m/s for warthog and 7m/s-10m/s for the Polaris GEM
We present a boundary-aware domain adaptation model for LiDAR scan full-scene semantic segmentation (LiDARNet).
This collaborative localization approach is built upon a distributed algorithm where individual and relative pose estimation techniques are combined for the group to localize against surrounding environments.