83 research outputs found
Using and evaluating the real-time spatial perception system hydra in real-world scenarios
Hydra is a real-time machine perception system released open source in 2022 as a package for
Robot Operating System (ROS). Machine perception systems like Hydra may play a role in
the engineering of the next generation of spatial AIs for autonomous robots. Hydra is in the
preliminary stages of its existence and does not come with intrinsic support for running on
custom datasets. This thesis primarily aims to find out whether the promised capabilities of
Hydra can be replicated. As well as to establish a workflow and guidelines for what
modifications to Hydra are needed to successfully run it
osmAG: Hierarchical Semantic Topometric Area Graph Maps in the OSM Format for Mobile Robotics
Maps are essential to mobile robotics tasks like localization and planning.
We propose the open street map (osm) XML based Area Graph file format to store
hierarchical, topometric semantic multi-floor maps of indoor and outdoor
environments, since currently no such format is popular within the robotics
community. Building on-top of osm we leverage the available open source editing
tools and libraries of osm, while adding the needed mobile robotics aspect with
building-level obstacle representation yet very compact, topometric data that
facilitates planning algorithms. Through the use of common osm keys as well as
custom ones we leverage the power of semantic annotation to enable various
applications. For example, we support planning based on robot capabilities, to
take the locomotion mode and attributes in conjunction with the environment
information into account. The provided C++ library is integrated into ROS. We
evaluate the performance of osmAG using real data in a global path planning
application on a very big osmAG map, demonstrating its convenience and
effectiveness for mobile robots.Comment: 7 page
Towards Robust Visual Localization in Challenging Conditions
Visual localization is a fundamental problem in computer vision, with a multitude of applications in robotics, augmented reality and structure-from-motion. The basic problem is to, based on one or more images, figure out the position and orientation of the camera which captured these images relative to some model of the environment. Current visual localization approaches typically work well when the images to be localized are captured under similar conditions compared to those captured during mapping. However, when the environment exhibits large changes in visual appearance, due to e.g. variations in weather, seasons, day-night or viewpoint, the traditional pipelines break down. The reason is that the local image features used are based on low-level pixel-intensity information, which is not invariant to these transformations: when the environment changes, this will cause a different set of keypoints to be detected, and their descriptors will be different, making the long-term visual localization problem a challenging one. In this thesis, five papers are included, which present work towards solving the problem of long-term visual localization. Two of the articles present ideas for how semantic information may be included to aid in the localization process: one approach relies only on the semantic information for visual localization, and the other shows how the semantics can be used to detect outlier feature correspondences. The third paper considers how the output from a monocular depth-estimation network can be utilized to extract features that are less sensitive to viewpoint changes. The fourth article is a benchmark paper, where we present three new benchmark datasets aimed at evaluating localization algorithms in the context of long-term visual localization. Lastly, the fifth article considers how to perform convolutions on spherical imagery, which in the future might be applied to learning local image features for the localization problem
A Cross-Season Correspondence Dataset for Robust Semantic Segmentation
In this paper, we present a method to utilize 2D-2D point matches between
images taken during different image conditions to train a convolutional neural
network for semantic segmentation. Enforcing label consistency across the
matches makes the final segmentation algorithm robust to seasonal changes. We
describe how these 2D-2D matches can be generated with little human interaction
by geometrically matching points from 3D models built from images. Two
cross-season correspondence datasets are created providing 2D-2D matches across
seasonal changes as well as from day to night. The datasets are made publicly
available to facilitate further research. We show that adding the
correspondences as extra supervision during training improves the segmentation
performance of the convolutional neural network, making it more robust to
seasonal changes and weather conditions.Comment: In Proc. CVPR 201
Using Image Sequences for Long-Term Visual Localization
Estimating the pose of a camera in a known scene, i.e., visual localization, is a core task for applications such as self-driving cars. In many scenarios, image sequences are available and existing work on combining single-image localization with odometry offers to unlock their potential for improving localization performance. Still, the largest part of the literature focuses on single-image localization and ignores the availability of sequence data. The goal of this paper is to demonstrate the potential of image sequences in challenging scenarios, e.g., under day-night or seasonal changes. Combining ideas from the literature, we describe a sequence-based localization pipeline that combines odometry with both a coarse and a fine localization module. Experiments on long-term localization datasets show that combining single-image global localization against a prebuilt map with a visual odometry / SLAM pipeline improves performance to a level where the extended CMU Seasons dataset can be considered solved. We show that SIFT features can perform on par with modern state-of-the-art features in our framework, despite being much weaker and a magnitude faster to compute. Our code is publicly available at github.com/rulllars
Active SLAM: A Review On Last Decade
This article presents a comprehensive review of the Active Simultaneous
Localization and Mapping (A-SLAM) research conducted over the past decade. It
explores the formulation, applications, and methodologies employed in A-SLAM,
particularly in trajectory generation and control-action selection, drawing on
concepts from Information Theory (IT) and the Theory of Optimal Experimental
Design (TOED). This review includes both qualitative and quantitative analyses
of various approaches, deployment scenarios, configurations, path-planning
methods, and utility functions within A-SLAM research. Furthermore, this
article introduces a novel analysis of Active Collaborative SLAM (AC-SLAM),
focusing on collaborative aspects within SLAM systems. It includes a thorough
examination of collaborative parameters and approaches, supported by both
qualitative and statistical assessments. This study also identifies limitations
in the existing literature and suggests potential avenues for future research.
This survey serves as a valuable resource for researchers seeking insights into
A-SLAM methods and techniques, offering a current overview of A-SLAM
formulation.Comment: 34 pages, 8 figures, 6 table
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