1 citations · 2 across the 11 of their papers we have counts for
12 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…
SurfSLAM: Sim-to-Real Underwater Stereo Reconstruction For Real-Time SLAM
Onur Bagoren, Seth Isaacson, Sacchin Sundar +6
Localization and mapping are core perceptual capabilities for underwater robots. Stereo cameras provide a low-cost means of directly estimating metric depth to support these tasks.…
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…
Reachability-based Trajectory Design via Exact Formulation of Implicit Neural Signed Distance Functions
Jonathan Michaux, Qingyi Chen, Challen Enninful Adu +2
Generating receding-horizon motion trajectories for autonomous vehicles in real-time while also providing safety guarantees is challenging. This is because a future trajectory need…
Safe Planning for Articulated Robots Using Reachability-based Obstacle Avoidance With Spheres
Jonathan Michaux, Adam Li, Qingyi Chen +3
Generating safe motion plans in real-time is necessary for the wide-scale deployment of robots in unstructured and human-centric environments. These motion plans must be safe to en…
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…
Serving Time: Real-Time, Safe Motion Planning and Control for Manipulation of Unsecured Objects
Zachary Brei, Jonathan Michaux, Bohao Zhang +2
A key challenge to ensuring the rapid transition of robotic systems from the industrial sector to more ubiquitous applications is the development of algorithms that can guarantee s…
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…