Philip Osteen

Peng Jiang, Philip Osteen, Maggie Wigness et al.

Semantic scene understanding is crucial for robust and safe autonomous navigation, particularly so in off-road environments. Recent deep learning advances for 3D semantic segmentation rely heavily on large sets of training data, however existing autonomy datasets either represent urban environments or lack multimodal off-road data. We fill this gap with RELLIS-3D, a multimodal dataset collected in an off-road environment, which contains annotations for 13,556 LiDAR scans and 6,235 images. 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. Additionally, we evaluate the current state-of-the-art deep learning semantic segmentation models on this dataset. Experimental results show that RELLIS-3D presents challenges for algorithms designed for segmentation in urban environments. This novel dataset provides the resources needed by researchers to continue to develop more advanced algorithms and investigate new research directions to enhance autonomous navigation in off-road environments. RELLIS-3D is available at https://github.com/unmannedlab/RELLIS-3D

Shaunak Kolhe, Peng Jiang, Maggie Wigness et al.

Radar offers unique advantages for localization in unstructured environments, including robustness to weather, lighting, and airborne particulates. While most prior work has studied radar odometry in urban, largely planar settings, its performance in off-road environments remains less understood. In this paper, we investigate the potential of radar for off-road odometry estimation and identify key challenges that arise from full $SE(3)$ vehicle motion, terrain-induced ground returns, and sparse or unstable features. To address these issues, we introduce two simple baselines: Radar-KISSICP, which applies motion compensation to generate 3D-aware radar pointclouds, and Radar-IMU, which leverages IMU preintegration to stabilize scan matching. Experiments on the Great Outdoors (GO) dataset demonstrate that these baselines improve trajectory estimation in challenging routes and provide a reference point for future development of radar odometry in off-road robotics.

Peng Jiang, Philip Osteen, Srikanth Saripalli

This paper proposes SemCal: an automatic, targetless, extrinsic calibration algorithm for a LiDAR and camera system using semantic information. We leverage a neural information estimator to estimate the mutual information (MI) of semantic information extracted from each sensor measurement, facilitating semantic-level data association. By using a matrix exponential formulation of the $se(3)$ transformation and a kernel-based sampling method to sample from camera measurement based on LiDAR projected points, we can formulate the LiDAR-Camera calibration problem as a novel differentiable objective function that supports gradient-based optimization methods. We also introduce a semantic-based initial calibration method using 2D MI-based image registration and Perspective-n-Point (PnP) solver. To evaluate performance, we demonstrate the robustness of our method and quantitatively analyze the accuracy using a synthetic dataset. We also evaluate our algorithm qualitatively on an urban dataset (KITTI360) and an off-road dataset (RELLIS-3D) benchmark datasets using both hand-annotated ground truth labels as well as labels predicted by the state-of-the-art deep learning models, showing improvement over recent comparable calibration approaches.

Peng Jiang, Philip Osteen, Srikanth Saripalli

We propose an algorithm for automatic, targetless, extrinsic calibration of a LiDAR and camera system using semantic information. We achieve this goal by maximizing mutual information (MI) of semantic information between sensors, leveraging a neural network to estimate semantic mutual information, and matrix exponential for calibration computation. Using kernel-based sampling to sample data from camera measurement based on LiDAR projected points, we formulate the problem as a novel differentiable objective function which supports the use of gradient-based optimization methods. We also introduce an initial calibration method using 2D MI-based image registration. Finally, we demonstrate the robustness of our method and quantitatively analyze the accuracy on a synthetic dataset and also evaluate our algorithm qualitatively on KITTI360 and RELLIS-3D benchmark datasets, showing improvement over recent comparable approaches.