5 citations · 5 across the 4 of their papers we have counts for
7 papers
Argos: Adapt Rich Geometric Priors for Generalizable Online Scene-Change-Detection
Ruihan Xu, Jiae Yoon, Kaichen Zhou +2
Robots operating in dynamic environments require reliable detection of how their surroundings change over time. Existing learning-based methods largely rely on pairwise 2D image fe…
Matisse: Evidence-Space Reasoning for Active 3D Reconstruction
Xihang Yu, Kaichen Zhou, Lorenzo Shaikewitz +4
How can a 3D reconstruction system acquire and retain useful information to understand the geometry of a scene from partial views under a limited computation budget? Existing activ…
Picasso: Holistic Scene Reconstruction with Physics-Constrained Sampling
Xihang Yu, Rajat Talak, Lorenzo Shaikewitz +1
In the presence of occlusions and measurement noise, geometrically accurate scene reconstructions -- which fit the sensor data -- can still be physically incorrect. For instance, w…
Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds
Lorenzo Shaikewitz, Charis Georgiou, Luca Carlone
Quantifying the uncertainty of an object's pose estimate is essential for robust control and planning. Although pose estimation is a well-studied robotics problem, attaching statis…
Non-submodular Visual Attention for Robot Navigation
Reza Vafaee, Kian Behzad, Milad Siami +2
This paper presents a task-oriented computational framework to enhance Visual-Inertial Navigation (VIN) in robots, addressing challenges such as limited time and energy resources.…
Category-Level Object Shape and Pose Estimation in Less Than a Millisecond
Lorenzo Shaikewitz, Tim Nguyen, Luca Carlone
Object shape and pose estimation is a foundational robotics problem, supporting tasks from manipulation to scene understanding and navigation. We present a fast local solver for sh…
A Certifiable Algorithm for Simultaneous Shape Estimation and Object Tracking
Lorenzo Shaikewitz, Samuel Ubellacker, Luca Carlone
Applications from manipulation to autonomous vehicles rely on robust and general object tracking to safely perform tasks in dynamic environments. We propose the first certifiably o…