Author name cluster

Elena Stumm

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

8 papers

1 author row

ICRA Conference 2017 Conference Paper

Map quality evaluation for visual localization

Hamza Merzic
Elena Stumm
Marcin Dymczyk
Roland Siegwart
Igor Gilitschenski

A variety of end-user devices involving keypoint-based mapping systems are about to hit the market e. g. as part of smartphones, cars, robotic platforms, or virtual and augmented reality applications. Thus, the generated map data requires automated evaluation procedures that do not require experienced personnel or ground truth knowledge of the underlying environment. A particularly important question enabling commercial applications is whether a given map is of sufficient quality for localization. This paper proposes a framework for predicting localization performance in the context of visual landmark-based mapping. Specifically, we propose an algorithm for predicting performance of vision-based localization systems from different poses within the map. To achieve this, a metric is defined that assigns a score to a given query pose based on the underlying map structure. The algorithm is evaluated on two challenging datasets involving indoor data generated using a handheld device and outdoor data from an autonomous fixed-wing unmanned aerial vehicle (UAV). Using these, we are able to show that the score provided by our method is highly correlated to the true localization performance. Furthermore, we demonstrate how the predicted map quality can be used within a belief based path planning framework in order to provide reliable trajectories through high-quality areas of the map.

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ICRA Conference 2017 Conference Paper

SegMatch: Segment based place recognition in 3D point clouds

Renaud Dubé
Daniel Dugas
Elena Stumm
Juan I. Nieto 0001
Roland Siegwart
Cesar Cadena 0001

Place recognition in 3D data is a challenging task that has been commonly approached by adapting image-based solutions. Methods based on local features suffer from ambiguity and from robustness to environment changes while methods based on global features are viewpoint dependent. We propose SegMatch, a reliable place recognition algorithm based on the matching of 3D segments. Segments provide a good compromise between local and global descriptions, incorporating their strengths while reducing their individual drawbacks. SegMatch does not rely on assumptions of `perfect segmentation', or on the existence of `objects' in the environment, which allows for reliable execution on large scale, unstructured environments. We quantitatively demonstrate that SegMatch can achieve accurate localization at a frequency of 1Hz on the largest sequence of the KITTI odometry dataset. We furthermore show how this algorithm can reliably detect and close loops in real-time, during online operation. In addition, the source code for the SegMatch algorithm is made publicly available.

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ICRA Conference 2017 Conference Paper

Visual place recognition with probabilistic voting

Mathias Gehrig
Elena Stumm
Timo Hinzmann
Roland Siegwart

We propose a novel scoring concept for visual place recognition based on nearest neighbor descriptor voting and demonstrate how the algorithm naturally emerges from the problem formulation. Based on the observation that the number of votes for matching places can be evaluated using a binomial distribution model, loop closures can be detected with high precision. By casting the problem into a probabilistic framework, we not only remove the need for commonly employed heuristic parameters but also provide a powerful score to classify matching and non-matching places. We present methods for both a 2D-2D image matching and a 2D-3D landmark matching based on the above scoring. The approach maintains accuracy while being efficient enough for online application through the use of compact (low-dimensional) descriptors and fast nearest neighbor retrieval techniques. The proposed methods are evaluated on several challenging datasets in varied environments, showing state-of-the-art results with high precision and high recall.

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IROS Conference 2016 Conference Paper

Appearance-based landmark selection for efficient long-term visual localization

Mathias Bürki
Igor Gilitschenski
Elena Stumm
Roland Siegwart
Juan I. Nieto 0001

In this paper, we present an online landmark selection method for distributed long-term visual localization systems in bandwidth-constrained environments. Sharing a common map for online localization provides a fleet of autonomous vehicles with the possibility to maintain and access a consistent map source, and therefore reduce redundancy while increasing efficiency. However, connectivity over a mobile network imposes strict bandwidth constraints and thus the need to minimize the amount of exchanged data. The wide range of varying appearance conditions encountered during long-term visual localization offers the potential to reduce data usage by extracting only those visual cues which are relevant at the given time. Motivated by this, we propose an unsupervised method of adaptively selecting landmarks according to how likely these landmarks are to be observable under the prevailing appearance condition. The ranking function this selection is based upon exploits landmark co-observability statistics collected in past traversals through the mapped area. Evaluation is performed over different outdoor environments, large time-scales and varying appearance conditions, including the extreme transition from day-time to night-time, demonstrating that with our appearance-dependent selection method, we can significantly reduce the amount of landmarks used for localization while maintaining or even improving the localization performance.

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IROS Conference 2016 Conference Paper

Erasing bad memories: Agent-side summarization for long-term mapping

Marcin Dymczyk
Thomas Schneider 0007
Igor Gilitschenski
Roland Siegwart
Elena Stumm

Precisely estimating the pose of an agent in a global reference frame is a crucial goal that unlocks a multitude of robotic applications, including autonomous navigation and collaboration. In order to achieve this, current state-of-the-art localization approaches collect data provided by one or more agents and create a single, consistent localization map, maintained over time. However, with the introduction of lengthier sorties and the growing size of the environments, data transfers between the backend server where the global map is stored and the agents are becoming prohibitively large. While some existing methods partially address this issue by building compact summary maps, the data transfer from the agents to the backend can still easily become unmanageable. In this paper, we propose a method that is designed to reduce the amount of data that needs to be transferred from the agent to the backend, functioning in large-scale, multi-session mapping scenarios. Our approach is based upon a landmark selection method that exploits information coming from multiple, possibly weak and correlated, landmark utility predictors; fused using learned feature coefficients. Such a selection yields a drastic reduction in data transfer while maintaining localization performance and the ability to efficiently summarize environments over time. We evaluate our approach on a data set that was autonomously collected in a dynamic indoor environment over a period of several months.

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ICRA Conference 2016 Conference Paper

Point cloud descriptors for place recognition using sparse visual information

Titus Cieslewski
Elena Stumm
Abel Gawel
Mike Bosse
Simon Lynen
Roland Siegwart

Place recognition is a core component in simultaneous localization and mapping (SLAM), limiting positional drift over space and time to unlock precise robot navigation. Determining which previously visited places belong together continues to be a highly active area of research as robotic applications demand increasingly higher accuracies. A large number of place recognition algorithms have been proposed, capable of consuming a variety of sensor data including laser, sonar and depth readings. The best performing solutions, however, have utilized visual information by either matching entire images or parts thereof. Most commonly, vision based approaches are inspired by information retrieval and utilize 3D-geometry information about the observed scene as a post-verification step. In this paper we propose to use the 3D-scene information from sparse-visual feature maps directly at the core of the place recognition pipeline. We propose a novel structural descriptor which aggregates sparse triangulated landmarks from SLAM into a compact signature. The resulting 3D-features provide a discriminative fingerprint to recognize places over seasonal and viewpoint changes which are particularly challenging for approaches based on sparse visual descriptors. We evaluate our system on publicly available datasets and show how its complementary nature can provide an improvement over visual place recognition.

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ICRA Conference 2015 Conference Paper

Location graphs for visual place recognition

Elena Stumm
Christopher Mei
Simon Lacroix
Margarita Chli

With the growing demand for deployment of robots in real scenarios, robustness in the perception capabilities for navigation lies at the forefront of research interest, as this forms the backbone of robotic autonomy. Existing place recognition approaches traditionally follow the feature-based bag-of-words paradigm in order to cut down on the richness of information in images. As structural information is typically ignored, such methods suffer from perceptual aliasing and reduced recall, due to the ambiguity of observations. In a bid to boost the robustness of appearance-based place recognition, we consider the world as a continuous constellation of visual words, while keeping track of their covisibility in a graph structure. Locations are queried based on their appearance, and modelled by their corresponding cluster of landmarks from the global covisibility graph, which retains important relational information about landmarks. Complexity is reduced by comparing locations by their graphs of visual words in a simplified manner. Test results show increased recall performance and robustness to noisy observations, compared to state-of-the-art methods.

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IROS Conference 2013 Conference Paper

Probabilistic place recognition with covisibility maps

Elena Stumm
Christopher Mei
Simon Lacroix

In order to diminish the influence of pose choice during appearance-based mapping, a more natural representation of location models is established using covisibility graphs. As the robot moves through the environment, visual landmarks are detected, and connected if seen as covisible. The introduction of a novel generative model allows relevant subgraphs of the covisibility map to be compared to a given query without needing to normalize over all previously seen locations. The use of probabilistic methods provides a unified framework to incorporate sensor error, perceptual aliasing, decision thresholds, and multiple location matches. The system is evaluated and compared with other state-of-the-art methods.

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