A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers

Placed, Julio A.; Strader, Jared; Carrillo, Henry; Atanasov, Nikolay; Indelman, Vadim; Carlone, Luca; Castellanos, José A.

doi:10.48550/arxiv.2207.00254

Cited by 5 publications

(9 citation statements)

References 200 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It occurs that the proposed bounds have riveting properties. To describe a pair of lower (lb (1) , lb (2) ) and a pair of upper bounds (ub (1) , ub (2) ) simultaneously, we omit the superscript. The lower bound is bounded by zero 0 ≤ lb and the upper bound is bounded by one ub ≥ 1.…”

Section: The Adaptationmentioning

confidence: 99%

“…The fact that we have a pair of lower (lb (1) , lb (2) ) and a pair of upper bounds (ub (1) , ub (2) ) raises the question which bound from each pair shall we adapt in case that a pair is inconclusive.…”

Section: The Adaptationmentioning

confidence: 99%

“…We also have an additional simulation with randomly drawing landmarks. For gtsam stability purposes we add random landmarks uniformly on the square [2, 5] × [2,5]. We also slightly changed the preliminary action sequence (Fig.…”

Section: B Maximal Feasible Returnmentioning

confidence: 99%

“…This assumption is the case in classical POMDP formulations. In contrast, numerous problems in robotics, such as informative planning tasks [1], active Simultaneous Localization and Mapping (SLAM) [2], and sensor placement problem [3] are explicitly concerned with decreasing uncertainty, thereby raising the need for planning with general belief-dependent reward functionals.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Simplified Continuous High Dimensional Belief Space Planning with Adaptive Probabilistic Belief-dependent Constraints

Zhitnikov¹,

Indelman²

2023

Preprint

View full text Add to dashboard Cite

Online decision making under uncertainty in partially observable domains, also known as Belief Space Planning, is a fundamental problem in robotics and Artificial Intelligence. Due to an abundance of plausible future unravelings, calculating an optimal course of action inflicts an enormous computational burden on the agent. Moreover, in many scenarios, e.g., information gathering, it is required to introduce a beliefdependent constraint. Prompted by this demand, in this paper, we consider a recently introduced probabilistic belief-dependent constrained POMDP. We present a technique to adaptively accept or discard a candidate action sequence with respect to a probabilistic belief-dependent constraint, before expanding a complete set of future observations samples and without any loss in accuracy. Moreover, using our proposed framework, we contribute an adaptive method to find a maximal feasible return (e.g., information gain) in terms of Value at Risk for the candidate action sequence with substantial acceleration. On top of that, we introduce an adaptive simplification technique for a probabilistically constrained setting. Such an approach provably returns an identical-quality solution while dramatically accelerating online decision making. Our universal framework applies to any beliefdependent constrained continuous POMDP with parametric beliefs, as well as nonparametric beliefs represented by particles. In the context of an information-theoretic constraint, our presented framework stochastically quantifies if a cumulative information gain along the planning horizon is sufficiently significant (e.g. for, information gathering, active SLAM). We apply our method to active SLAM, a highly challenging problem of high dimensional Belief Space Planning. Extensive realistic simulations corroborate the superiority of our proposed ideas.

show abstract

Section: The Adaptationmentioning

confidence: 99%

Section: The Adaptationmentioning

confidence: 99%

Section: B Maximal Feasible Returnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Simplified Continuous High Dimensional Belief Space Planning with Adaptive Probabilistic Belief-dependent Constraints

Zhitnikov¹,

Indelman²

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Suppose that the data refers to the robot's observations, for example, camera images or LiDAR point clouds, and the unknown target function is the ground-truth representation of the environment. In that case, RIG can be considered an Active Mapping problem (Placed et al, 2022). Mapping uncertainty can come from aleatoric uncertainty inherent in measurement noise and epistemic uncertainty due to unknown model parameters and data scarcity (Krause and Guestrin 2007).…”

Section: Active Localizationmentioning

confidence: 99%

Adaptive Robotic Information Gathering via non-stationary Gaussian processes

Chen

Khardon

Liu

2023

The International Journal of Robotics Research

View full text Add to dashboard Cite

Robotic Information Gathering (RIG) is a foundational research topic that answers how a robot (team) collects informative data to efficiently build an accurate model of an unknown target function under robot embodiment constraints. RIG has many applications, including but not limited to autonomous exploration and mapping, 3D reconstruction or inspection, search and rescue, and environmental monitoring. A RIG system relies on a probabilistic model’s prediction uncertainty to identify critical areas for informative data collection. Gaussian processes (GPs) with stationary kernels have been widely adopted for spatial modeling. However, real-world spatial data is typically non-stationary—different locations do not have the same degree of variability. As a result, the prediction uncertainty does not accurately reveal prediction error, limiting the success of RIG algorithms. We propose a family of non-stationary kernels named Attentive Kernel (AK), which is simple and robust and can extend any existing kernel to a non-stationary one. We evaluate the new kernel in elevation mapping tasks, where AK provides better accuracy and uncertainty quantification over the commonly used stationary kernels and the leading non-stationary kernels. The improved uncertainty quantification guides the downstream informative planner to collect more valuable data around the high-error area, further increasing prediction accuracy. A field experiment demonstrates that the proposed method can guide an Autonomous Surface Vehicle (ASV) to prioritize data collection in locations with significant spatial variations, enabling the model to characterize salient environmental features.

show abstract

Real-Time SLAM Based on Dynamic Feature Point Elimination in Dynamic Environment

Gao,

Li,

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Slam (simultaneous localization and mapping) play an important role in the field of artificial and driverless intelligence. A real-time dynamic visual SLAM algorithm based on an object detection network is proposed to address the robustness and camera localization accuracy issues caused by dynamic objects in indoor dynamic scenes. The YOLOv5s model, which has the smallest depth and feature map width in the YOLOv5 series, is chosen as the object detection network. The backbone network is replaced with the lightweight ShuffleNetv2 network. Experimental results on the VOC2007 dataset show that the YOLOv5-LITE model reduces the network parameters by 41.89%and speeds up the runtime by 39.00% compared to the YOLOv5s model. A motion level division strategy is adopted to provide prior information to the object detection network. In the tracking thread of the visual SLAM system, a parallel thread combining the improved object detection network and multi-view geometry is introduced to eliminate dynamic feature points. The experimental results demonstrate that in dynamic scenes, the proposed algorithm improves the camera localization accuracy by an average of 85.38% compared to ORB-SLAM2. Finally, experiments in a real environment are conducted to validate the effectiveness of the algorithm.

show abstract

A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers

Cited by 5 publications

References 200 publications

Simplified Continuous High Dimensional Belief Space Planning with Adaptive Probabilistic Belief-dependent Constraints

Simplified Continuous High Dimensional Belief Space Planning with Adaptive Probabilistic Belief-dependent Constraints

Adaptive Robotic Information Gathering via non-stationary Gaussian processes

Real-Time SLAM Based on Dynamic Feature Point Elimination in Dynamic Environment

Contact Info

Product

Resources

About