6 accepted papers
Semantic grids can be useful representations of the scene around an autonomous system. By having information about the layout of the space around itself, a robot can leverage this type of representation for crucial tasks such as navigation or tracking. By fusing information from multiple sensors, ro…
Multi-modal systems have the capacity of producing more reliable results than systems with a single modality in road detection due to perceiving different aspects of the scene. We focus on using raw sensor inputs instead of, as it is typically done in many SOTA works, leveraging architectures that r
Ground plane estimation and ground point segmentation is a crucial precursor for many applications in robotics and intelligent vehicles like navigable space detection and occupancy grid generation, 3D object detection, point cloud matching for localization and registration for mapping. In this paper…
Semantic information provides a valuable source for scene understanding around autonomous vehicles in order to plan their actions and make decisions. However, varying weather conditions reduce the accuracy of the semantic segmentation. We propose a method to adapt to varying weather conditions witho
In an autonomous vehicle setting, we propose a method for the estimation of a semantic grid, i.e. a bird's eye grid centered on the car's position and aligned with its driving direction, which contains high-level semantic information about the environment and its actors. Each grid cell contains a se…
In this work, we consider long-term topological place learning and present an approach that enables the robot to learn in an unsupervised, organized and incremental manner. The knowledge associated with the previously visited places is internally stored in the form of bubble descriptor semantic tree…