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Unified Data Collection for Visual-Inertial Calibration via Deep Reinforcement Learning
(2022)2022 International Conference on Robotics and Automation (ICRA)Visual-inertial sensors have a wide range of applications in robotics. However, good performance often requires different sophisticated motion routines to accurately calibrate camera intrinsics and inter-sensor extrinsics. This work presents a novel formulation to learn a motion policy to be executed on a robot arm for automatic data collection for calibrating intrinsics and extrinsics jointly. Our approach models the calibration process ...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 -
Dynamic Object Aware LiDAR SLAM based on Automatic Generation of Training Data
(2021)2021 IEEE International Conference on Robotics and Automation (ICRA)Highly dynamic environments, with moving objects such as cars or humans, can pose a performance challenge for LiDAR SLAM systems that assume largely static scenes. To overcome this challenge and support the deployment of robots in real world scenarios, we propose a complete solution for a dynamic object aware LiDAR SLAM algorithm. This is achieved by leveraging a real-time capable neural network that can detect dynamic objects, thus ...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 -
SemSegMap – 3D Segment-based Semantic Localization
(2021)2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)Localization is an essential task for mobile autonomous robotic systems that want to use pre-existing maps or create new ones in the context of SLAM. Today, many robotic platforms are equipped with high-accuracy 3D LiDAR sensors, which allow a geometric mapping, and cameras able to provide semantic cues of the environment. Segment-based mapping and localization have been applied with great success to 3D point-cloud data, while semantic ...Conference Paper -
Learning Camera Miscalibration Detection
(2020)2020 IEEE International Conference on Robotics and Automation (ICRA)Self-diagnosis and self-repair are some of the key challenges in deploying robotic platforms for long-term real-world applications. One of the issues that can occur to a robot is miscalibration of its sensors due to aging, environmental transients, or external disturbances. Precise calibration lies at the core of a variety of applications, due to the need to accurately perceive the world. However, while a lot of work has focused on ...Conference Paper