Feature Based Multi-Hypothesis Map Representation for Localization in Non-Static Environments

Nielsen, Kristin; Hendeby, Gustaf

doi:10.23919/fusion49751.2022.9841255

Cited by 3 publications

(11 citation statements)

References 22 publications

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“…This section contains a short description of the feature based multi-hypothesis map representation presented in [15].…”

Section: A Multi-hypothesis Mapmentioning

confidence: 99%

“…However, this quickly becomes computationally intractable since the number of mode sequences grows exponentially with time. In [15] it is shown how the number of hypotheses are reduced in the pure localization problem by utilizing the knowledge that landmarks do not move while in FOV. By deducing which mode is currently active, the number of concurrent hypotheses can often be reduced to only one.…”

Section: B Multi-hypothesis Slammentioning

confidence: 99%

“…However, this likelihood estimate only considers observations that are matched to landmarks in the map, and when different hypotheses are evaluated also missed detections and false alarms should be considered. In the state estimator based on the multihypothesis map representation, presented in [15], the likelihood is computed from equations inspired by target tracking applications. In principal the same computations can be used for the SLAM application.…”

Section: A Measurement Likelihoodmentioning

confidence: 99%

“…1) Negative Information: When the multi-hypothesis map representation is used in a pure state estimation scenario, as in [15], negative information can be utilized in the sense that if a landmark expected to be observed is not, the mode probabilities in that landmark group will shift towards other modes. This usage of negative information does not directly transfer to the SLAM application since not all modes are known a priori.…”

Section: B Hypotheses Branchingmentioning

confidence: 99%

“…In [15] we presented a feature based multi-hypothesis map representation to be used for localization in non-static environments where uniquely identifiable landmarks can have multiple discrete modes. This paper extends the work by considering the creation and updating of such maps.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Hypothesis SLAM for Non-Static Environments With Reoccurring Landmarks

Nielsen

Hendeby

2023

IEEE Trans. Intell. Veh.

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A static world assumption is often used when considering the simultaneous localization and mapping (SLAM) problem. In reality, especially when long-term autonomy is the objective, this is not valid. This paper studies a scenario where uniquely identifiable landmarks can attend multiple discrete positions, not known a priori. Based on a feature based multi-hypothesis map representation, a multi-hypothesis SLAM algorithm is developed inspired by target tracking theory. The creation of such a map is merged into the SLAM framework allowing any available SLAM method to solve the underlying mapping and localization problem for each hypothesis. A recursively updated hypothesis score allows for hypothesis rejection and prevents exponential growth in the number of hypotheses. The developed method is evaluated in an underground mine application, where physical barriers can be moved in between multiple distinct positions. Simulations are conducted in this environment showing the benefits of the multi-hypothesis approach compared to executing a standard SLAM algorithm. Practical considerations as well as suitable approximations are elaborated upon and experiments on real data further validates the simulated results and show that the multi-hypothesis approach has similar performance in reality as in simulation.

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“…This section contains a short description of the feature based multi-hypothesis map representation presented in [15].…”

Section: A Multi-hypothesis Mapmentioning

confidence: 99%

Section: B Multi-hypothesis Slammentioning

confidence: 99%

Section: A Measurement Likelihoodmentioning

confidence: 99%

Section: B Hypotheses Branchingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Hypothesis SLAM for Non-Static Environments With Reoccurring Landmarks

Nielsen

Hendeby

2023

IEEE Trans. Intell. Veh.

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Survey of maps of dynamics for mobile robots

Kucner

Magnusson

Mghames

et al. 2023

The International Journal of Robotics Research

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Robotic mapping provides spatial information for autonomous agents. Depending on the tasks they seek to enable, the maps created range from simple 2D representations of the environment geometry to complex, multilayered semantic maps. This survey article is about maps of dynamics (MoDs), which store semantic information about typical motion patterns in a given environment. Some MoDs use trajectories as input, and some can be built from short, disconnected observations of motion. Robots can use MoDs, for example, for global motion planning, improved localization, or human motion prediction. Accounting for the increasing importance of maps of dynamics, we present a comprehensive survey that organizes the knowledge accumulated in the field and identifies promising directions for future work. Specifically, we introduce field-specific vocabulary, summarize existing work according to a novel taxonomy, and describe possible applications and open research problems. We conclude that the field is mature enough, and we expect that maps of dynamics will be increasingly used to improve robot performance in real-world use cases. At the same time, the field is still in a phase of rapid development where novel contributions could significantly impact this research area.

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Hypothesis selection with Monte Carlo tree search for feature-based simultaneous localization and mapping in non-static environments

Nielsen,

Hendeby

2023

The International Journal of Robotics Research

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A static world assumption is often used when considering the simultaneous localization and mapping (SLAM) problem. In reality, especially when long-term autonomy is the objective, this is not a valid assumption. This paper studies a scenario where landmarks can occupy multiple discrete positions at different points in time, where each possible position is added to a multi-hypothesis map representation. A selector-mixture distribution is introduced and used in the observation model. Each landmark position hypothesis is associated with one component in the mixture. The landmark movements are modeled by a discrete Markov chain and the Monte Carlo tree search algorithm is suggested to be used as component selector. The non-static environment model is further incorporated into the factor graph formulation of the SLAM problem and is solved by iterating between estimating discrete variables with a component selector and optimizing continuous variables with an efficient state-of-the-art nonlinear least squares SLAM solver. The proposed non-static SLAM system is validated in numerical simulation and with a publicly available dataset by showing that a non-static environment can successfully be navigated.

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Feature Based Multi-Hypothesis Map Representation for Localization in Non-Static Environments

Cited by 3 publications

References 22 publications

Multi-Hypothesis SLAM for Non-Static Environments With Reoccurring Landmarks

Multi-Hypothesis SLAM for Non-Static Environments With Reoccurring Landmarks

Survey of maps of dynamics for mobile robots

Hypothesis selection with Monte Carlo tree search for feature-based simultaneous localization and mapping in non-static environments

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