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 …

Active learning is a decision-making process. In both abstract and physical settings, active
learning demands both analysis and action. This is a review of active learning in robotics …

We propose an information-theoretic planning approach that enables mobile robots to
autonomously construct dense 3D maps in a computationally efficient manner. Inspired by …

At the present time, water and sewer pipe networks are predominantly inspected manually.
In the near future, smart cities will perform intelligent autonomous monitoring of buried pipe …

We investigate the problem of planning under uncertainty, with application to mobile
robotics. We propose a probabilistic framework in which the robot bases its decisions on the …

For long-term simultaneous planning, localization and mapping (SPLAM), a robot should be
able to continuously update its map according to the dynamic changes of the environment …

The most common way to deal with the uncertainty present in noisy sensorial perception and
action is to model the problem with a probabilistic framework. Maximum likelihood …

Most of the existing robotic exploration schemes use occupancy grid representations and
geometric targets known as frontiers. The occupancy grid representation relies on the …

Two-dimensional (2-D)/3-D pose-graph simultaneous localization and mapping (SLAM) is a
problem of estimating a set of poses based on noisy measurements of relative rotations and …

Belief space planning provides a principled framework to compute motion plans that
explicitly gather information from sensing, as necessary, to reduce uncertainty about the …