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MOZARD: Multi-Modal Localization for Autonomous Vehicles in Urban Outdoor Environments
(2020)2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)Visually poor scenarios are one of the main sources of failure in visual localization systems in outdoor environments. To address this challenge, we present MOZARD, a multi-modal localization system for urban outdoor environments using vision and LiDAR. By fusing key point based visual multi-session information with semantic data, an improved localization recall can be achieved across vastly different appearance conditions. In particular ...Conference Paper -
Spherical Multi-Modal Place Recognition for Heterogeneous Sensor Systems
(2021)2021 IEEE International Conference on Robotics and Automation (ICRA)In this paper, we propose a robust end-to-end multi-modal pipeline for place recognition where the sensor systems can differ from the map building to the query. Our approach operates directly on images and LiDAR scans without requiring any local feature extraction modules. By projecting the sensor data onto the unit sphere, we learn a multi-modal descriptor of partially overlapping scenes using a spherical convolutional neural network. ...Conference Paper -
Out-of-Distribution Detection for Automotive Perception
(2021)2021 IEEE International Intelligent Transportation Systems Conference (ITSC)Neural networks (NNs) are widely used for object classification in autonomous driving. However, NNs can fail on input data not well represented by the training dataset, known as out-of-distribution (OOD) data. A mechanism to detect OOD samples is important for safety-critical applications, such as automotive perception, to trigger a safe fallback mode. NNs often rely on softmax normalization for confidence estimation, which can lead to ...Conference Paper -
Panoptic Multi-TSDFs: a Flexible Representation for Online Multi-resolution Volumetric Mapping and Long-term Dynamic Scene Consistency
(2022)2022 International Conference on Robotics and Automation (ICRA)For robotic interaction in environments shared with other agents, access to volumetric and semantic maps of the scene is crucial. However, such environments are inevitably subject to long-term changes, which the map needs to account for. We thus propose panoptic multi-TSDFs as a novel representation for multi-resolution volumetric mapping in changing environments. By leveraging high-level information for 3D reconstruction, our proposed ...Conference Paper