Probabilistic semantic mapping for urban autonomous driving applications
Recent advancements in statistical learning and computational abilities have enabled
autonomous vehicle technology to develop at a much faster rate. While many of the …
autonomous vehicle technology to develop at a much faster rate. While many of the …
MR-GMMapping: Communication efficient multi-robot mapping system via Gaussian mixture model
Collaborative perception in unknown environments is a critical task for multi-robot systems.
Without external positioning, multi-robot mapping systems have relied on the transfer of …
Without external positioning, multi-robot mapping systems have relied on the transfer of …
Feature-based occupancy map-merging for collaborative slam
One of the most frequently used approaches to represent collaborative mapping are
probabilistic occupancy grid maps. These maps can be exchanged and integrated among …
probabilistic occupancy grid maps. These maps can be exchanged and integrated among …
Semantic-driven autonomous visual navigation for unmanned aerial vehicles
P Yue, J Xin, Y Zhang, Y Lu… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Aiming at the autonomous navigation of unmanned aerial vehicles (UAVs) in complex and
unknown environments, this article combines transfer reinforcement learning theory with an …
unknown environments, this article combines transfer reinforcement learning theory with an …
Dense incremental metric-semantic mapping via sparse gaussian process regression
We develop an online probabilistic metric-semantic mapping approach for autonomous
robots relying on streaming RGB-D observations. We cast this problem as a Bayesian …
robots relying on streaming RGB-D observations. We cast this problem as a Bayesian …
Semantic point cloud mapping of LiDAR based on probabilistic uncertainty modeling for autonomous driving
LiDAR-based Simultaneous Localization And Mapping (SLAM), which provides
environmental information for autonomous vehicles by map building, is a major challenge …
environmental information for autonomous vehicles by map building, is a major challenge …
Camera-LIDAR integration: Probabilistic sensor fusion for semantic mapping
An automated vehicle operating in an urban environment must be able to perceive and
recognise objects and obstacles in a three-dimensional world for navigation and path …
recognise objects and obstacles in a three-dimensional world for navigation and path …
Distributed consistent multi-robot semantic localization and mapping
V Tchuiev, V Indelman - IEEE Robotics and Automation Letters, 2020 - ieeexplore.ieee.org
We present an approach for multi-robot consistent distributed localization and semantic
mapping in an unknown environment, considering scenarios with classification ambiguity …
mapping in an unknown environment, considering scenarios with classification ambiguity …
Bayesian nonparametric object association for semantic SLAM
J Zhang, L Yuan, T Ran, Q Tao… - IEEE Robotics and …, 2021 - ieeexplore.ieee.org
Semantic simultaneous localization and mapping (SLAM) can provide the foundation for
robots to perform more advanced tasks than traditional geometric SLAM. Object association …
robots to perform more advanced tasks than traditional geometric SLAM. Object association …
Hierarchical probabilistic fusion framework for matching and merging of 3-d occupancy maps
Fusing 3-D maps generated by multiple robots in real/semi-real time distributed mapping
systems are addressed in this paper. A 3-D occupancy grid-based approach for mapping is …
systems are addressed in this paper. A 3-D occupancy grid-based approach for mapping is …