Search Results for author: Andrei Cramariuc

Found 10 papers, 7 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

NeuralBlox: Real-Time Neural Representation Fusion for Robust Volumetric Mapping

1 code implementation18 Oct 2021 Stefan Lionar, Lukas Schmid, Cesar Cadena, Roland Siegwart, Andrei Cramariuc

We present a novel 3D mapping method leveraging the recent progress in neural implicit representation for 3D reconstruction.

3D Reconstruction

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)

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)

Driving Through Ghosts: Behavioral Cloning with False Positives

no code implementations29 Aug 2020 Andreas Bühler, Adrien Gaidon, Andrei Cramariuc, Rares Ambrus, Guy Rosman, Wolfram Burgard

In this work, we propose a behavioral cloning approach that can safely leverage imperfect perception without being conservative.

Autonomous Driving

Learning Camera Miscalibration Detection

1 code implementation24 May 2020 Andrei Cramariuc, Aleksandar Petrov, Rohit Suri, Mayank Mittal, Roland Siegwart, Cesar Cadena

Self-diagnosis and self-repair are some of the key challenges in deploying robotic platforms for long-term real-world applications.

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

SegMap: Segment-based mapping and localization using data-driven descriptors

2 code implementations27 Sep 2019 Renaud Dubé, Andrei Cramariuc, Daniel Dugas, Hannes Sommer, Marcin Dymczyk, Juan Nieto, Roland Siegwart, Cesar Cadena

We therefore present SegMap: a map representation solution for localization and mapping based on the extraction of segments in 3D point clouds.

Autonomous Driving Retrieval

SegMap: 3D Segment Mapping using Data-Driven Descriptors

1 code implementation25 Apr 2018 Renaud Dubé, Andrei Cramariuc, Daniel Dugas, Juan Nieto, Roland Siegwart, Cesar Cadena

While current methods extract descriptors for the single task of localization, SegMap leverages a data-driven descriptor in order to extract meaningful features that can also be used for reconstructing a dense 3D map of the environment and for extracting semantic information.

Data Compression

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