Author: stachnis

2017-11: Code Available: Extended Version of Visual Place Recognition using Hashing by Olga Vysotska

Visual Place Recognition using Hashing by Olga Vysotska and Cyrill Stachniss

Localization system is available on GitHub

Given two sequences of images represented by the descriptors, the code constructs a data association graph and performs a search within this graph, so that for every query image, the code computes a matching hypothesis to an image in a database sequence as well as matching hypotheses for the previous images. The matching procedure can be performed in two modes: feature based and cost matrix based mode. The new version using an own hashing approach to quickly relocalize.

This code is related to the following publications:
O. Vysotska and C. Stachniss, “Relocalization under Substantial Appearance Changes using Hashing,” in Proc. of the 9th Workshop on Planning, Perception, and Navigation for Intelligent Vehicles at the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2017.

2017-09: Work on Semi-Supervised Crop-Weed Detection becomes IROS 2017 Best Application Paper Finalist

The work “Semi-Supervised Online Visual Crop and Weed Classification in Precision Farming Exploiting Plant Arrangement” by Philipp Lottes and Cyrill stachniss, presented at IROS 2017 in Vancouver, becomes IROS 2017 Best Application Paper Finalist.

lottes-iros17-award-1400

Abstract – Precision farming robots offer a great potential for reducing the amount of agro-chemicals that is required in the fields through a targeted, per-plant intervention. To achieve this, robots must be able to reliably distinguish crops from weeds on different fields and across growth stages. In this paper, we tackle the problem of separating crops from weeds reliably while requiring only a minimal amount of training data through a semi-supervised approach. We exploit the fact that most crops are planted in rows with a similar spacing along the row, which in turn can be used to initialize a vision-based classifier requiring only minimal user efforts to adapt it to a new field. We implemented our approach using C++ and ROS and thoroughly tested it on real farm robots operating in different countries. The experiments presented in this paper show that with around 1 min of labeling time, we can achieve classification results with an accuracy of more than 95% in real sugar beet fields in Germany and Switzerland.

2017-09: Code Available: MPR: Multi-Cue Photometric Registration by B. Della Corte, I. Bogoslavskyi, C. Stachniss, and G. Grisett

A General Framework for Flexible Multi-Cue Photometric Point Cloud Registration by Bartolomeo Della Corte, Igor Bogoslavskyi, Cyrill Stachniss, and Giorgio Grisetti

MPR: Multi-Cue Photometric Registration is available on GitLab

The ability to build maps is a key functionality for the majority of mobile robots. A central ingredient to most mapping systems is the registration or alignment of the recorded sensor data. In this paper, we present a general methodology for photometric registration that can deal with multiple different cues. We provide examples for registering RGBD as well as 3D LIDAR data. In contrast to popular point cloud registration approaches such as ICP our method does not rely on explicit data association and exploits multiple modalities such as raw range and image data streams. Color, depth, and normal information are handled in an uniform manner and the registration is obtained by minimizing the pixel-wise difference between two multi-channel images. We developed a flexible and general framework and implemented our approach inside that framework. We also released our implementation as open source C++ code. The experiments show that our approach allows for an accurate registration of the sensor data without requiring an explicit data association or model-specific adaptations to datasets or sensors. Our approach exploits the different cues in a natural and consistent way and the registration can be done at framerate for a typical range or imaging sensor.

This code is related to the following publication:
“A General Framework for Flexible Multi-Cue Photometric Point Cloud Registration” by Bartolomeo Della Corte, Igor Bogoslavskyi, Cyrill Stachniss, Giorgio Grisetti. Submitted to ICRA 2018 and available on arXiv:1709.05945.

2017-08: Olga Vysotska receives a Google Award for the ACM womENcourage 2017

Olga Vysotska receives a Google Award for the ACM womENcourage 2017 in Barcelona in September 2017. The award is given through the European ACM-W committee (ACM-EW). The ACM-WE vision is a transformed European professional and scholarly landscape where women are supported and inspired to pursue their dreams and ambitions to find fulfillment in the computing field.

2017-06: Code Available: Online Place Recognition by Olga Vysotska

Online Place Recognition by Graph-Based Matching of Image Sequences by Olga Vysotska and Cyrill Stachniss

Online Place Recognition is available on GitHub

Given two sequences of images represented by the descriptors, the code constructs a data association graph and performs a search within this graph, so that for every query image, the code computes a matching hypothesis to an image in a database sequence as well as matching hypothesis for the previous images. The matching procedure can be perfomed in two modes: feature based and cost matrix based mode. For more theoretical details, please refer to our paper Lazy data association for image sequence matching under substantial appearance changes.

This code is related to the following publication:
O. Vysotska and C. Stachniss, “Lazy Data Association For Image Sequences Matching Under Substantial Appearance Changes,” IEEE Robotics and Automation Letters (RA-L)and IEEE International Conference on Robotics & Automation (ICRA), vol. 1, iss. 1, pp. 1-8, 2016. doi:10.1109/LRA.2015.2512936.