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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 -
NeuralBlox: Real-Time Neural Representation Fusion for Robust Volumetric Mapping
(2021)2021 International Conference on 3D Vision (3DV)We present a novel 3D mapping method leveraging the recent progress in neural implicit representation for 3D reconstruction. Most existing state-of-the-art neural implicit representation methods are limited to object-level reconstructions and can not incrementally perform updates given new data. In this work, we propose a fusion strategy and training pipeline to incrementally build and update neural implicit representations that enable ...Conference Paper -
Precise Robot Localization in Architectural 3D Plans
(2021)ISARC Proceedings ~ Proceedings of the 38th International Symposium on Automation and Robotics in Construction (ISARC)This paper presents a localization system for mobile robots enabling precise localization in inaccurate building models. The approach leverages local referencing to counteract inherent deviations between as-planned and as-built data for locally accurate registration. We further fuse a novel camera-based robust outlier detector with LiDAR data to reject a wide range of outlier measurements from clutter, dynamic objects, and sensor failures. ...Conference Paper -
3D VSG: Long-term Semantic Scene Change Prediction through 3D Variable Scene Graphs
(2023)2023 IEEE International Conference on Robotics and Automation (ICRA)Numerous applications require robots to operate in environments shared with other agents, such as humans or other robots. However, such shared scenes are typically subject to different kinds of long-term semantic scene changes. The ability to model and predict such changes is thus crucial for robot autonomy. In this work, we formalize the task of semantic scene variability estimation and identify three main varieties of semantic scene ...Conference Paper -
SphNet: A Spherical Network for Semantic Pointcloud Segmentation
(2023)2023 IEEE International Conference on Robotics and Automation (ICRA)Semantic segmentation for robotic systems can enable a wide range of applications, from self-driving cars and augmented reality systems to domestic robots. We argue that a spherical representation is a natural one for egocentric pointclouds. Thus, in this work, we present a novel framework exploiting such a representation of LiDAR pointclouds for the task of semantic segmentation. Our approach is based on a spherical convolutional neural ...Conference Paper -
Self-Improving Semantic Perception for Indoor Localisation
(2021)5th Annual Conference on Robot Learning (CoRL 2021)We propose a novel robotic system that can improve its semantic perception during deployment. Contrary to the established approach of learning semantics from large datasets and deploying fixed models, we propose a framework in which semantic models are continuously updated on the robot to adapt to the deployment environments. Our system therefore tightly couples multi-sensor perception and localisation to continuously learn from self-supervised ...Conference Paper -
CalQNet - Detection of calibration quality for life-long stereo camera setups
(2021)2021 IEEE Intelligent Vehicles Symposium (IV)Many mobile robotic platforms rely on an accurate knowledge of the extrinsic calibration parameters, especially systems performing visual stereo matching. Although a number of accurate stereo camera calibration methods have been developed, which provide good initial 'factory' calibrations, the determined parameters can lose their validity over time as the sensors are exposed to environmental conditions and external effects. Thus, on ...Conference Paper -
Pixel-wise Anomaly Detection in Complex Driving Scenes
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)The inability of state-of-the-art semantic segmentation methods to detect anomaly instances hinders them from being deployed in safety-critical and complex applications, such as autonomous driving. Recent approaches have focused on either leveraging segmentation uncertainty to identify anomalous areas or re-synthesizing the image from the semantic label map to find dissimilarities with the input image. In this work, we demonstrate that ...Conference Paper -
Fishyscapes: A Benchmark for Safe Semantic Segmentation in Autonomous Driving
(2020)2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)Conference Paper