5 accepted papers
We present the first systematic analysis of multimodal large language models (MLLMs) in personalized question-answering requiring ego-grounding - the ability to understand the camera-wearer in egocentric videos. To this end, we introduce MyEgo, the first egocentric VideoQA dataset designed to evalua
Rearrangement operations form the crux of interactions between humans and their environment. The ability to generate natural, fluid sequences of this operation is of essential value in AR/VR and CG. Bridging a gap in the field, our study introduces FAVOR: a novel dataset for Full-body AR-driven Virt…
This paper studies the problem of multi-robot cooperative visual-inertial localization where each robot is equipped with only a single camera and IMU. We develop two cooperative visual-inertial odometry (C-VIO) algorithms within the multi-state constraint Kalman filter (MSCKF) framework, in which ea…
In this paper we present a consistent and distributed state estimator for multi-robot cooperative localization (CL) which efficiently fuses environmental features and loop-closure constraints across time and robots. In particular, we leverage covariance intersection (CI) to allow each robot to only…
In this paper, we present a novel distributed algorithm to track a moving object's state by utilizing a heterogenous mobile robot network in a three-dimensional (3-D) environment, wherein the robots' poses (positions and orientations) are unknown. Each robot is equipped with a monocular camera and a…