Dominik Schlegel — accepted AI-conference papers

2020

Plug-and-Play SLAM: A Unified SLAM Architecture for Modularity and Ease of Use

Mirco Colosi, Irvin Aloise, Tiziano Guadagnino, Dominik Schlegel, Bartolomeo Della Corte, Kai O. Arras +1

IROS 2020poster

Simultaneous Localization and Mapping (SLAM) is considered a mature research field with numerous applications and publicly available open-source systems. Despite this maturity, existing SLAM systems often rely on ad-hoc implementations or are tailored to predefined sensor setups. In this work, we ta…

Cited by 18 Source ↗Scholar ↗

2019

Adding Cues to Binary Feature Descriptors for Visual Place Recognition

Dominik Schlegel, Giorgio Grisetti

ICRA 2019poster

In this paper we propose an approach to embed multi-dimensional continuous cues in binary feature descriptors used for visual place recognition. The embedding is achieved by extending each feature descriptor with a binary string that encodes a cue and supports the Hamming distance metric. Augmenting…

Cited by 3 Source ↗code ↗Scholar ↗

2018

HBST: A Hamming Distance Embedding Binary Search Tree for Feature-Based Visual Place Recognition

Dominik Schlegel, Giorgio Grisetti

RA-L 2018

Reliable and efficient visual place recognition is a major building block of modern SLAM systems. Leveraging on our prior work, in this letter, we present a Hamming distance embedding binary search tree (HBST) approach for binary descriptor matching and image retrieval. HBST allows for descriptor se

Cited by 43 Source ↗Scholar ↗

2018

ProSLAM: Graph SLAM from a Programmer's Perspective

Dominik Schlegel, Mirco Colosi, Giorgio Grisetti

ICRA 2018poster

In this paper we present ProSLAM, a lightweight open-source stereo visual SLAM system designed with simplicity in mind. This work stems from the experience gathered by the authors while teaching SLAM and aims at providing a highly modular system that can be easily implemented and understood. Rather…

Cited by 94 Source ↗code ↗Scholar ↗

2016

Visual localization and loop closing using decision trees and binary features

Dominik Schlegel, Giorgio Grisetti

IROS 2016poster

In this paper we present an approach for efficiently retrieving the most similar image, based on point-to-point correspondences, within a sequence that has been acquired through continuous camera movement. Our approach is entailed to the use of standardized binary feature descriptors and exploits th…

Cited by 14 Source ↗Scholar ↗