Active simultaneous localization and mapping (SLAM) is the problem of planning and
controlling the motion of a robot to build the most accurate and complete model of the …

Indoor environment model reconstruction has emerged as a significant and challenging task
in terms of the provision of a semantically rich and geometrically accurate indoor model …

Modern autonomous driving system is characterized as modular tasks in sequential order,
ie, perception, prediction, and planning. In order to perform a wide diversity of tasks and …

This approach, designed for mobile robots equipped with synchro-drives, is derived directly
from the motion dynamics of the robot. In experiments, the dynamic window approach safely …

We introduce a new approach for exploration based on the concept of frontiers, regions on
the boundary between open space and unexplored space. By moving to new frontiers, a …

We propose Occupancy Flow Fields, a new representation for motion forecasting of multiple
agents, an important task in autonomous driving. Our representation is a spatio-temporal …

Recent advances in 3D sensing have created unique challenges for computer vision. One
fundamental challenge is finding a good representation for 3D sensor data. Most popular …

Autonomous robots must be able to learn and maintain models of their environments.
Research on mobile robot navigation has produced two major paradigms for mapping …

An overview on state of the art automotive radar usage is presented and the changing
requirements from detection and ranging towards radar based environmental understanding …

Frontier-based exploration directs mobile robots to regions on the boundary between
unexplored space and space that is known to be open. Previously, we have demonstrated …