High-Speed Robot Navigation using Predicted Occupancy Maps

Katyal, Kapil D.; Polevoy, Adam; Moore, J. Russell; Knuth, Craig; Popek, Katie M.

doi:10.1109/icra48506.2021.9561034

Cited by 13 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fehr et al [231] use a neural network to augment the measurements of a depth sensor and Ramakrishnan et al [232] directly predict augmented OG maps beyond the sensor's field-of-view using auto-encoders (AE). Rather than using raw sensor measurements, Katyal et al [233] and Hayoun et al [234] extend an input OG map beyond the lineof-sight also using AE. Shrestha et al [235] predict maps of occupancy probabilities instead with variational AE.…”

Section: A Prediction Beyond Line-of-sightmentioning

confidence: 99%

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

Placed

Strader

Carrillo³

et al. 2023

IEEE Trans. Robot.

View full text Add to dashboard Cite

Active Simultaneous Localization and Mapping (SLAM) is the problem of planning and controlling the motion of a robot to build the most accurate and complete model of the surrounding environment. Since the first foundational work in active perception appeared, more than three decades ago, this field has received increasing attention across different scientific communities. This has brought about many different approaches and formulations, and makes a review of the current trends necessary and extremely valuable for both new and experienced researchers. In this work, we survey the state-ofthe-art in active SLAM and take an in-depth look at the open challenges that still require attention to meet the needs of modern applications. After providing a historical perspective, we present a unified problem formulation and review the well-established modular solution scheme, which decouples the problem into three stages that identify, select, and execute potential navigation actions. We then analyze alternative approaches, including beliefspace planning and deep reinforcement learning techniques, and review related work on multi-robot coordination. The manuscript concludes with a discussion of new research directions, addressing reproducible research, active spatial perception, and practical applications, among other topics.

show abstract

Section: A Prediction Beyond Line-of-sightmentioning

confidence: 99%

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

Placed

Strader

Carrillo³

et al. 2023

IEEE Trans. Robot.

View full text Add to dashboard Cite

show abstract

“…Fehr et al [221] use a neural network to augment the measurements of a depth sensor and Ramakrishnan et al [222] directly predict augmented OG maps beyond the sensor's field-of-view using auto-encoders (AE). Rather than using raw sensor measurements, Katyal et al [223] and Hayoun et al [224] extend an input OG map beyond the lineof-sight also using AE. Shrestha et al [225] predict maps of occupancy probabilities instead with variational AE.…”

Section: A Prediction Beyond Line-of-sightmentioning

confidence: 99%

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

Placed¹,

Strader²,

Carrillo³

et al. 2022

Preprint

View full text Add to dashboard Cite

Active Simultaneous Localization and Mapping (SLAM) is the problem of planning and controlling the motion of a robot to build the most accurate and complete model of the surrounding environment. Since the first foundational work in active perception appeared, more than three decades ago, this field has received increasing attention across different scientific communities. This has brought about many different approaches and formulations, and makes a review of the current trends necessary and extremely valuable for both new and experienced researchers. In this work, we survey the state-of-the-art in active SLAM and take an in-depth look at the open challenges that still require attention to meet the needs of modern applications. After providing a historical perspective, we present a unified problem formulation and review the classical solution scheme, which decouples the problem into three stages that identify, select, and execute potential navigation actions. We then analyze alternative approaches, including belief-space planning and modern techniques based on deep reinforcement learning, and review related work on multi-robot coordination. The manuscript concludes with a discussion of new research directions, addressing reproducible research, active spatial perception, and practical applications, among other topics.

show abstract

“…However, existing methods face limitations in complex environments and high-speed navigation scenarios. For instance, [15] introduces novel perception algorithms and a controller that incorporate predicted occupancy maps for high-speed navigation. Despite its potential, the method struggles to handle complex and obstacle-dense environments due to simplistic scene design and a lower map update frequency (≈ 3 Hz).…”

Section: B Navigation In Predicted Mapsmentioning

confidence: 99%

Energy-Efficient Autonomous Navigation of Solar-Powered UAVs for Surveillance of Mobile Ground Targets in Urban Environments

Huang

Savkin

2020

Energies

View full text Add to dashboard Cite

In this paper, we consider the navigation of a group of solar-powered unmanned aerial vehicles (UAVs) for periodical monitoring of a set of mobile ground targets in urban environments. We consider the scenario where the number of targets is larger than that of the UAVs, and the targets spread in the environment, so that the UAVs need to carry out a periodical surveillance. The existence of tall buildings in urban environments brings new challenges to the periodical surveillance mission. They may not only block the Line-of-Sight (LoS) between a UAV and a target, but also create some shadow region, so that the surveillance may become invalid, and the UAV may not be able to harvest energy from the sun. The periodical surveillance problem is formulated as an optimization problem to minimize the target revisit time while accounting for the impact of the urban environment. A nearest neighbour based navigation method is proposed to guide the movements of the UAVs. Moreover, we adopt a partitioning scheme to group targets for the purpose of narrowing UAVs’ moving space, which further reduces the target revisit time. The effectiveness of the proposed method is verified via computer simulations.

show abstract

High-Speed Robot Navigation using Predicted Occupancy Maps

Cited by 13 publications

References 23 publications

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

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

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

Energy-Efficient Autonomous Navigation of Solar-Powered UAVs for Surveillance of Mobile Ground Targets in Urban Environments

Contact Info

Product

Resources

About