4 accepted papers
A central challenge for multi-robot systems is fusing independently gathered perception data into a unified representation. Despite progress in Collaborative SLAM (C-SLAM), benchmarking remains hindered by the scarcity of dedicated multi-robot datasets. Many evaluations instead partition single-robo…
We present a dataset for evaluating the performance of visual-inertial odometry (VIO) systems employing an onboard light source. The dataset consists of 39 sequences, recorded in mines, tunnels, and other dark environments, totaling more than 160 minutes of stereo camera video and IMU data. In each…
This letter assesses an adaptive approach to fault recovery in autonomous robotic space operations, which uses indicators of opportunity, such as physiological state measurements and observations of past human assistant performance, to inform future selections. We validated our reinforcement learnin
This letter considers the problem of how robots in long-term space operations can learn to choose appropriate sources of assistance to recover from failures. Current assistant selection methods for failure handling are based on manually specified static lookup tables or policies, which are not respo