Search Results for author: Florian Tschopp

Found 10 papers, 6 papers with code

Descriptellation: Deep Learned Constellation Descriptors

no code implementations1 Mar 2022 Chunwei Xing, Xinyu Sun, Andrei Cramariuc, Samuel Gull, Jen Jen Chung, Cesar Cadena, Roland Siegwart, Florian Tschopp

However, handcrafted topological descriptors are hard to tune and not robust to environmental noise, drastic perspective changes, object occlusion or misdetections.

Simultaneous Localization and Mapping

Unified Data Collection for Visual-Inertial Calibration via Deep Reinforcement Learning

1 code implementation30 Sep 2021 Yunke Ao, Le Chen, Florian Tschopp, Michel Breyer, Andrei Cramariuc, Roland Siegwart

Our approach models the calibration process compactly using model-free deep reinforcement learning to derive a policy that guides the motions of a robotic arm holding the sensor to efficiently collect measurements that can be used for both camera intrinsic calibration and camera-IMU extrinsic calibration.

reinforcement-learning Reinforcement Learning (RL)

Superquadric Object Representation for Optimization-based Semantic SLAM

no code implementations20 Sep 2021 Florian Tschopp, Juan Nieto, Roland Siegwart, Cesar Cadena

Introducing semantically meaningful objects to visual Simultaneous Localization And Mapping (SLAM) has the potential to improve both the accuracy and reliability of pose estimates, especially in challenging scenarios with significant view-point and appearance changes.

Object Recognition Semantic SLAM

3D3L: Deep Learned 3D Keypoint Detection and Description for LiDARs

1 code implementation25 Mar 2021 Dominic Streiff, Lukas Bernreiter, Florian Tschopp, Marius Fehr, Roland Siegwart

Furthermore, 3D feature-based registration methods have never quite reached the robustness of 2D methods in visual SLAM.

Keypoint Detection

Hough2Map -- Iterative Event-based Hough Transform for High-Speed Railway Mapping

1 code implementation16 Feb 2021 Florian Tschopp, Cornelius von Einem, Andrei Cramariuc, David Hug, Andrew William Palmer, Roland Siegwart, Margarita Chli, Juan Nieto

As a basis for a localization system we propose a complete on-board mapping pipeline able to map robust meaningful landmarks, such as poles from power lines, in the vicinity of the vehicle.

Vocal Bursts Intensity Prediction

Learning Trajectories for Visual-Inertial System Calibration via Model-based Heuristic Deep Reinforcement Learning

1 code implementation4 Nov 2020 Le Chen, Yunke Ao, Florian Tschopp, Andrei Cramariuc, Michel Breyer, Jen Jen Chung, Roland Siegwart, Cesar Cadena

Visual-inertial systems rely on precise calibrations of both camera intrinsics and inter-sensor extrinsics, which typically require manually performing complex motions in front of a calibration target.

reinforcement-learning Reinforcement Learning (RL)

LCD -- Line Clustering and Description for Place Recognition

1 code implementation21 Oct 2020 Felix Taubner, Florian Tschopp, Tonci Novkovic, Roland Siegwart, Fadri Furrer

In this paper, we introduce a novel learning-based approach to place recognition, using RGB-D cameras and line clusters as visual and geometric features.

Image Retrieval Object Recognition +2

IDOL: A Framework for IMU-DVS Odometry using Lines

no code implementations13 Aug 2020 Cedric Le Gentil, Florian Tschopp, Ignacio Alzugaray, Teresa Vidal-Calleja, Roland Siegwart, Juan Nieto

The method's front-end extracts event clusters that belong to line segments in the environment whereas the back-end estimates the system's trajectory alongside the lines' 3D position by minimizing point-to-line distances between individual events and the lines' projection in the image space.


Go Fetch: Mobile Manipulation in Unstructured Environments

no code implementations2 Apr 2020 Kenneth Blomqvist, Michel Breyer, Andrei Cramariuc, Julian Förster, Margarita Grinvald, Florian Tschopp, Jen Jen Chung, Lionel Ott, Juan Nieto, Roland Siegwart

With humankind facing new and increasingly large-scale challenges in the medical and domestic spheres, automation of the service sector carries a tremendous potential for improved efficiency, quality, and safety of operations.

Motion Planning

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