17 citations · 40 across the 16 of their papers we have counts for
19 papers
Online Geometric Change Detection via Scene Decomposition
David Thorne, Samuel Jia Cong Chua, Nakul Joshi +4
Autonomous robots are increasingly deployed on long duration single- and multi-session missions in dynamic environments, where the ability to identify environmental changes such as…
PinNet: Keypoint-Aware Learned Local Descriptors with Geometric Embedding for Loop Closure in LiDAR SLAM
Yanlong Ma, Nakul S. Joshi, Christa S. Robison +2
Loop closure is essential to reduce drift and build globally consistent maps in large-scale environments. However, reliable loop closure with only geometric information from, e.g.,…
Pushing Radar Odometry Beyond the Pavement: Current Capabilities and Challenges
Shaunak Kolhe, Peng Jiang, Maggie Wigness +4
Radar offers unique advantages for localization in unstructured environments, including robustness to weather, lighting, and airborne particulates. While most prior work has studie…
Geometric Multi-Session Map Merging with Learned Local Descriptors
Yanlong Ma, Nakul S. Joshi, Christa S. Robison +2
Multi-session map merging is crucial for extended autonomous operations in large-scale environments. In this paper, we present GMLD, a learning-based local descriptor framework for…
OptMap: Geometric Map Distillation via Submodular Maximization
David Thorne, Nathan Chan, Christa S. Robison +2
Autonomous robots rely on geometric maps to inform a diverse set of perception and decision-making algorithms. As autonomy requires reasoning and planning on multiple scales, each…
Learning Smooth State-Dependent Traversability from Dense Point Clouds
Zihao Dong, Alan Papalia, Leonard Jung +4
A key open challenge in off-road autonomy is that the traversability of terrain often depends on the vehicle's state. In particular, some obstacles are only traversable from some o…
GO: The Great Outdoors Multimodal Dataset
Peng Jiang, Kasi Viswanath, Akhil Nagariya +8
The Great Outdoors (GO) dataset is a multi-modal annotated data resource aimed at advancing ground robotics research in unstructured environments. Existing off-road datasets often…
LiDAR Inertial Odometry And Mapping Using Learned Registration-Relevant Features
Zihao Dong, Jeff Pflueger, Leonard Jung +5
SLAM is an important capability for many autonomous systems, and modern LiDAR-based methods offer promising performance. However, for long duration missions, existing works that ei…
Submodular Optimization for Keyframe Selection & Usage in SLAM
David Thorne, Nathan Chan, Yanlong Ma +3
Keyframes are LiDAR scans saved for future reference in Simultaneous Localization And Mapping (SLAM), but despite their central importance most algorithms leave choices of which sc…
M2P2: A Multi-Modal Passive Perception Dataset for Off-Road Mobility in Extreme Low-Light Conditions
Aniket Datar, Anuj Pokhrel, Mohammad Nazeri +8
Long-duration, off-road, autonomous missions require robots to continuously perceive their surroundings regardless of the ambient lighting conditions. Most existing autonomy system…
PIETRA: Physics-Informed Evidential Learning for Traversing Out-of-Distribution Terrain
Xiaoyi Cai, James Queeney, Tong Xu +8
Self-supervised learning is a powerful approach for developing traversability models for off-road navigation, but these models often struggle with inputs unseen during training. Ex…
EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy
Xiaoyi Cai, Siddharth Ancha, Lakshay Sharma +7
Traversing terrain with good traction is crucial for achieving fast off-road navigation. Instead of manually designing costs based on terrain features, existing methods learn terra…
RAMP: A Risk-Aware Mapping and Planning Pipeline for Fast Off-Road Ground Robot Navigation
Lakshay Sharma, Michael Everett, Donggun Lee +3
A key challenge in fast ground robot navigation in 3D terrain is balancing robot speed and safety. Recent work has shown that 2.5D maps (2D representations with additional 3D infor…
Probabilistic Traversability Model for Risk-Aware Motion Planning in Off-Road Environments
Xiaoyi Cai, Michael Everett, Lakshay Sharma +2
A key challenge in off-road navigation is that even visually similar terrains or ones from the same semantic class may have substantially different traction properties. Existing wo…
Semantic keypoint-based pose estimation from single RGB frames
Karl Schmeckpeper, Philip R. Osteen, Yufu Wang +6
This paper presents an approach to estimating the continuous 6-DoF pose of an object from a single RGB image. The approach combines semantic keypoints predicted by a convolutional…
SemCal: Semantic LiDAR-Camera Calibration using Neural MutualInformation Estimator
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 est…
Calibrating LiDAR and Camera using Semantic Mutual information
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 informat…
RELLIS-3D Dataset: Data, Benchmarks and Analysis
Peng Jiang, Philip Osteen, Maggie Wigness +1
Semantic scene understanding is crucial for robust and safe autonomous navigation, particularly so in off-road environments. Recent deep learning advances for 3D semantic segmentat…
Experimental Evaluation of 3D-LIDAR Camera Extrinsic Calibration
Subodh Mishra, Philip R. Osteen, Gaurav Pandey +1
In this paper we perform an experimental comparison of three different target based 3D-LIDAR camera calibration algorithms. We briefly elucidate the mathematical background behind…