10 accepted papers
Simultaneous localization and mapping is a critical capability for autonomous systems. Traditional SLAM approaches often rely on visual or LiDAR sensors and face significant challenges in adverse conditions such as low light or featureless environments. To overcome these limitations, we propose a no…
Reliable odometry in highly dynamic environments remains challenging when it relies on ICP-based registration: ICP assumes near-static scenes and degrades in repetitive or low-texture geometry. We introduce Dynamic-ICP, a Doppler-aware registration framework. The method (i) estimates ego translation
Localization of autonomous vehicles in existing maps is crucial for reliable navigation. Using previously constructed maps allows vehicles to estimate their pose without the inherent odometry drift. Building such maps involves aligning data recorded at different times and maintaining the map over ti
Simultaneous localization and mapping is a critical capability for autonomous systems. Traditional SLAM approaches often rely on visual or LiDAR sensors and face significant challenges in adverse conditions such as low light or featureless environments. To overcome these limitations, we propose a no
Odometry is crucial for the navigation of autonomous vehicles in unknown environments. While cameras and LiDARs are commonly used to estimate the ego-motion of a vehicle, these sensors face limitations under bad lighting and severe weather conditions. Automotive radars overcome these challenges, but
Simultaneous localization and mapping are essential components for the operation of autonomous vehicles in unknown environments. While localization focuses on estimating the vehicle's pose, mapping captures the surrounding environment to enhance future localization and decision-making. Localization
Semantic scene understanding, including the perception and classification of moving agents, is essential to enabling safe and robust driving behaviours of autonomous vehicles. Cameras and LiDARs are commonly used for semantic scene understanding. However, both sensor modalities face limitations in a
Robots and autonomous vehicles should be aware of what happens in their surroundings. The segmentation and tracking of moving objects are essential for reliable path planning, including collision avoidance. We investigate this estimation task for vehicles using radar sensing. We address moving insta…
Odometry and mapping play a pivotal role in the navigation of autonomous vehicles. In this paper, we address the problem of pose estimation and map creation using only radar sensors. We focus on two odometry estimation approaches followed by a mapping step. The first one is a new point-to-point ICP…
Localization is a crucial component for the navigation of autonomous vehicles. It encompasses global localization and place recognition, allowing a system to identify locations that have been mapped or visited before. Place recognition is commonly approached using cameras or LiDARs. However, these s