An open visual-inertial mapping framework: maplab

This repository contains maplab, an open, research-oriented visual-inertial mapping framework, written in C++, for creating, processing and manipulating multi-session maps. On the one hand, maplab can be considered as a ready-to-use visual-inertial mapping and localization system. On the other hand, maplab provides the research community with a collection of multi-session mapping tools that include map merging, visual-inertial batch optimization, and loop closure. Furthermore, it includes an online frontend, ROVIOLI, that can create visual-inertial maps and also track a global drift-free pose within a localization map. https://github.com/ethz-asl/maplab

Appearance-Based Landmark Selection for Visual Localization

Mathias Bürki, Cesar Cadena, Igor Gilitschenski, Roland Siegwart and Juan Nieto Journal of Fields Robotics (JFR) 2019 Visual localization in outdoor environments is subject to varying appearance conditions rendering it difficult to match current camera images against a previously recorded map. Although it is possible to extend the respective maps to allow precise localization across a wide range of differing appearance conditions, these maps quickly grow in size and become impractical to handle on a mobile robotic platform. To address this problem, we present a landmark selection algorithm that exploits appearance co‐observability for efficient visual localization in outdoor environments. Based...

OREOS: Oriented Recognition of 3D Point Clouds in Outdoor Scenarios

Lukas Schaupp, Mathias Buerki, Renaud Dube, Roland Siegwart, and Cesar Cadena IEEE/RJS Int. Conference on Intelligent RObots and Systems (IROS) 2019 We introduce a novel method for oriented place recognition with 3D LiDAR scans. A Convolutional Neural Network is trained to extract compact descriptors from single 3D LiDAR scans. These can be used both to retrieve near-by place candidates from a map, and to estimate the yaw discrepancy needed for bootstrapping local registration methods. We employ a triplet loss function for training and use a hard negative mining strategy to further increase the performance of our descriptor extractor. In an...

VIZARD: Reliable Visual Localization for Autonomous Vehicles in Urban Outdoor Environments

Mathias Buerki, Lukas Schaupp, Marcyn Dymczyk, Renaud Dube, Cesar Cadena, Roland Siegwart, and Juan Nieto IEEE Intelligent Vehicles Symposium (IV) 2019 Changes in appearance is one of the main sources of failure in visual localization systems in outdoor environments. To address this challenge, we present VIZARD, a visual localization system for urban outdoor environments. By combining a local localization algorithm with the use of multi-session maps, a high localization recall can be achieved across vastly different appearance conditions. The fusion of the visual localization constraints with wheel-odometry in a state estimation framework further guarantees smooth and accurate pose estimates. In...

Object Classification Based on Unsupervised Learned Multi-Modal Features for Overcoming Sensor Failures

Julia Nitsch, Juan Nieto, Roland Siegwart, Max Schmidt, and Cesar Cadena IEEE International Conference on Robotics and Automation (ICRA) 2019 For autonomous driving applications it is critical to know which type of road users and road side infrastructure are present to plan driving manoeuvres accordingly. Therefore autonomous cars are equipped with different sensor modalities to robustly perceive its environment. However, for classification modules based on machine learning techniques it is challenging to overcome unseen sensor noise. This work presents an object classification module operating on unsupervised learned multi-modal features with the ability to overcome gradual or total sensor failure. A...

SegMap: Segment-based Mapping and Localization using Data-driven Descriptors

Renaud Dube, Andrei Cramariuc1, Daniel Dugas, Hannes Sommer, Marcin Dymczyk, Juan Nieto, Roland Siegwart, and Cesar Cadena International Journal of Robotics Research (IJRR) 2019 Precisely estimating a robot’s pose in a prior, global map is a fundamental capability for mobile robotics, e.g. autonomous driving or exploration in disaster zones. This task, however, remains challenging in unstructured, dynamic environments, where local features are not discriminative enough and global scene descriptors only provide coarse information. We therefore present SegMap: a map representation solution for localization and mapping based on the extraction of segments in 3D point clouds. Working at the level of...

Multiple Hypothesis Semantic Mapping for Robust Data Association

Lukas Bernreiter, Abel Gawel, Hannes Sommer, Juan Nieto, Roland Siegwart and Cesar Cadena IEEE Robotics and Automation Letters, 2019 We present a semantic mapping approach with multiple hypothesis tracking for data association. As semantic information has the potential to overcome ambiguity in measurements and place recognition, it forms an eminent modality for autonomous systems. This is particularly evident in urban scenarios with several similar-looking surroundings. Nevertheless, it requires the handling of a non-Gaussian and discrete random variable coming from object detectors. Previous methods facilitate semantic information for global localization and data association to reduce the instance ambiguity between the landmarks....

Empty Cities: Image Inpainting for a Dynamic-Object-Invariant Space

Berta Bescos, Jose Neira, Roland Siegwart, and Cesar Cadena IEEE International Conference on Robotics and Automation (ICRA) 2019 In this paper we present an end-to-end deep learning framework to turn images that show dynamic content, such as vehicles or pedestrians, into realistic static frames. This objective encounters two main challenges: detecting all the dynamic objects, and inpainting the static occluded background with plausible imagery. The former challenge is addressed by the use of a convolutional network that learns a multiclass semantic segmentation of the image. The second problem is approached with a conditional generative adversarial model that, taking as input...

Deliverable 9.1

Project Web-page This deliverable corresponds to Task 9.2: Set-up of fully functional project web site. In the current stage the web site offers detailed information about the project and of the partners in UP-Drive consortium. The web site will be updated continuously to communicate to the public any project related news. pdf

Deliverable 5.2

First development and integration cycle of lifelong mapping This deliverable describes the lifelong mapping framework after the first development & integration cycle. All components, notably the metric and semantic map, the metric online localization, the semantic data aggregation and the map summarization are functional and integrated on the vehicles, fulfill their basic purposes and interact with each other in a limited fashion. All components deliver first evaluation results. pdf