397 citations · 544 across the 18 of their papers we have counts for
19 papers
AirSplan: Risk-Aware Motion Planning for Quadrotors in Cluttered 3D Gaussian Splats
Seth Isaacson, William Hong, Katherine A. Skinner +1
Quadrotors are increasingly deployed in applications such as agriculture, infrastructure inspection, and maintenance. In each of these applications, the robot must navigate complex…
HumanSplatHMR: Closing the Loop Between Human Mesh Recovery and Gaussian Splatting Avatar
Yeheng Zong, Pou-Chun Kung, Yike Pan +4
Accurately recovering human pose and appearance from video is an essential component of scene reconstruction, with applications to motion capture, motion prediction, virtual realit…
RadarSplat: Radar Gaussian Splatting for High-Fidelity Data Synthesis and 3D Reconstruction of Autonomous Driving Scenes
Pou-Chun Kung, Skanda Harisha, Ram Vasudevan +2
High-Fidelity 3D scene reconstruction plays a crucial role in autonomous driving by enabling novel data generation from existing datasets. This allows simulating safety-critical sc…
These Magic Moments: Differentiable Uncertainty Quantification of Radiance Field Models
Parker Ewen, Hao Chen, Seth Isaacson +3
This paper introduces a novel approach to uncertainty quantification for radiance fields by leveraging higher-order moments of the rendering equation. Uncertainty quantification is…
Conformalized Reachable Sets for Obstacle Avoidance With Spheres
Yongseok Kwon, Jonathan Michaux, Seth Isaacson +4
Safe motion planning algorithms are necessary for deploying autonomous robots in unstructured environments. Motion plans must be safe to ensure that the robot does not harm humans…
Let's Make a Splan: Risk-Aware Trajectory Optimization in a Normalized Gaussian Splat
Jonathan Michaux, Seth Isaacson, Challen Enninful Adu +6
Neural Radiance Fields and Gaussian Splatting have recently transformed computer vision by enabling photo-realistic representations of complex scenes. However, they have seen limit…
SPOT: Point Cloud Based Stereo Visual Place Recognition for Similar and Opposing Viewpoints
Spencer Carmichael, Rahul Agrawal, Ram Vasudevan +1
Recognizing places from an opposing viewpoint during a return trip is a common experience for human drivers. However, the analogous robotics capability, visual place recognition (V…
Dataset and Benchmark: Novel Sensors for Autonomous Vehicle Perception
Spencer Carmichael, Austin Buchan, Mani Ramanagopal +3
Conventional cameras employed in autonomous vehicle (AV) systems support many perception tasks, but are challenged by low-light or high dynamic range scenes, adverse weather, and f…
LONER: LiDAR Only Neural Representations for Real-Time SLAM
Seth Isaacson, Pou-Chun Kung, Mani Ramanagopal +2
This paper proposes LONER, the first real-time LiDAR SLAM algorithm that uses a neural implicit scene representation. Existing implicit mapping methods for LiDAR show promising res…
Uncertainty-Aware Acoustic Localization and Mapping for Underwater Robots
Jingyu Song, Onur Bagoren, Katherine A. Skinner
For underwater vehicles, robotic applications have the added difficulty of operating in highly unstructured and dynamic environments. Environmental effects impact not only the dyna…
WaterNeRF: Neural Radiance Fields for Underwater Scenes
Advaith Venkatramanan Sethuraman, Manikandasriram Srinivasan Ramanagopal, Katherine A. Skinner
Underwater imaging is a critical task performed by marine robots for a wide range of applications including aquaculture, marine infrastructure inspection, and environmental monitor…
CLONeR: Camera-Lidar Fusion for Occupancy Grid-aided Neural Representations
Alexandra Carlson, Manikandasriram Srinivasan Ramanagopal, Nathan Tseng +3
Recent advances in neural radiance fields (NeRFs) achieve state-of-the-art novel view synthesis and facilitate dense estimation of scene properties. However, NeRFs often fail for l…
AstroVision: Towards Autonomous Feature Detection and Description for Missions to Small Bodies Using Deep Learning
Travis Driver, Katherine Skinner, Mehregan Dor +1
Missions to small celestial bodies rely heavily on optical feature tracking for characterization of and relative navigation around the target body. While deep learning has led to g…
Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landing
Kento Tomita, Katherine A. Skinner, Koki Ho
Hazard detection is critical for enabling autonomous landing on planetary surfaces. Current state-of-the-art methods leverage traditional computer vision approaches to automate the…
Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade
Abigail R. Azari, John B. Biersteker, Ryan M. Dewey +13
Machine learning (ML) methods can expand our ability to construct, and draw insight from large datasets. Despite the increasing volume of planetary observations, our field has seen…
Sensor Transfer: Learning Optimal Sensor Effect Image Augmentation for Sim-to-Real Domain Adaptation
Alexandra Carlson, Katherine A. Skinner, Ram Vasudevan +1
Performance on benchmark datasets has drastically improved with advances in deep learning. Still, cross-dataset generalization performance remains relatively low due to the domain…
DispSegNet: Leveraging Semantics for End-to-End Learning of Disparity Estimation from Stereo Imagery
Junming Zhang, Katherine A. Skinner, Ram Vasudevan +1
Recent work has shown that convolutional neural networks (CNNs) can be applied successfully in disparity estimation, but these methods still suffer from errors in regions of low-te…
Modeling Camera Effects to Improve Visual Learning from Synthetic Data
Alexandra Carlson, Katherine A. Skinner, Ram Vasudevan +1
Recent work has focused on generating synthetic imagery to increase the size and variability of training data for learning visual tasks in urban scenes. This includes increasing th…
WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images
Jie Li, Katherine A. Skinner, Ryan M. Eustice +1
This paper reports on WaterGAN, a generative adversarial network (GAN) for generating realistic underwater images from in-air image and depth pairings in an unsupervised pipeline u…