A survey on active simultaneous localization and mapping: State of the art and new frontiers
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 …
controlling the motion of a robot to build the most accurate and complete model of the …
Active mapping and robot exploration: A survey
Simultaneous localization and mapping responds to the problem of building a map of the
environment without any prior information and based on the data obtained from one or more …
environment without any prior information and based on the data obtained from one or more …
Voronoi-based multi-robot autonomous exploration in unknown environments via deep reinforcement learning
Autonomous exploration is an important application of multi-vehicle systems, where a team
of networked robots are coordinated to explore an unknown environment collaboratively …
of networked robots are coordinated to explore an unknown environment collaboratively …
Tartanair: A dataset to push the limits of visual slam
We present a challenging dataset, the TartanAir, for robot navigation tasks and more. The
data is collected in photo-realistic simulation environments with the presence of moving …
data is collected in photo-realistic simulation environments with the presence of moving …
Reinforcement learning for mobile robotics exploration: A survey
LC Garaffa, M Basso, AA Konzen… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Efficient exploration of unknown environments is a fundamental precondition for modern
autonomous mobile robot applications. Aiming to design robust and effective robotic …
autonomous mobile robot applications. Aiming to design robust and effective robotic …
A Review on Viewpoints and Path Planning for UAV-Based 3-D Reconstruction
Unmanned aerial vehicles (UAVs) are widely used platforms to carry data capturing sensors
for various applications. The reason for this success can be found in many aspects: the high …
for various applications. The reason for this success can be found in many aspects: the high …
Eao-slam: Monocular semi-dense object slam based on ensemble data association
Object-level data association and pose estimation play a fundamental role in semantic
SLAM, which remain unsolved due to the lack of robust and accurate algorithms. In this …
SLAM, which remain unsolved due to the lack of robust and accurate algorithms. In this …
Augmenting reinforcement learning with transformer-based scene representation learning for decision-making of autonomous driving
Decision-making for urban autonomous driving is challenging due to the stochastic nature of
interactive traffic participants and the complexity of road structures. Although reinforcement …
interactive traffic participants and the complexity of road structures. Although reinforcement …
Ariadne: A reinforcement learning approach using attention-based deep networks for exploration
In autonomous robot exploration tasks, a mobile robot needs to actively explore and map an
unknown environment as fast as possible. Since the environment is being revealed during …
unknown environment as fast as possible. Since the environment is being revealed during …
Reinforcement learning-based complete area coverage path planning for a modified hTrihex robot
One of the essential attributes of a cleaning robot is to achieve complete area coverage.
Current commercial indoor cleaning robots have fixed morphology and are restricted to …
Current commercial indoor cleaning robots have fixed morphology and are restricted to …