Maplets: An Efficient Approach for Cooperative SLAM Map Building Under Communication and Computation Constraints

Brink, Kevin; Sherrill, Ryan E.; Godwin, Jamie; Zhang, Jincheng; Willis, Andrew

doi:10.1109/plans46316.2020.9109931

Cited by 3 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, DVO Odometry performs interpolations for depth images, intensity images, depth gradients in

directions, and intensity gradients in

directions at every iteration, as a result of which, it is very computationally expensive. This can be addressed by directly solving for the correspondences between plane fits from two plane images [ 30 ]. We use the compact representation of the world in terms of planar algebraic surfaces, i.e., surfaces having equation

, to establish the likelihood that a given hypothesized plane image pair can be aligned with the intent to piece together large geometric map regions in a manner similar to puzzle-solving.…”

Section: Methodsmentioning

confidence: 99%

“…The algorithm propagates local to global contexts to solve for a global correspondence [ 27 , 28 ] and manages to reduce bandwidth requirements by transmitting a subsets of the map information as a collection of sparse features. Recent research has explored the use of compact geometric representations like planes to reduce the map size [ 23 , 29 , 30 ]. These sparse representations give rise to sparse map data that may however contain large holes and ambiguous regions.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Low-Bandwidth and Compute-Bound RGB-D Planar Semantic SLAM

Zhang

Ganesh

Volle

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

Visual simultaneous location and mapping (SLAM) using RGB-D cameras has been a necessary capability for intelligent mobile robots. However, when using point-cloud map representations as most RGB-D SLAM systems do, limitations in onboard compute resources, and especially communication bandwidth can significantly limit the quantity of data processed and shared. This article proposes techniques that help address these challenges by mapping point clouds to parametric models in order to reduce computation and bandwidth load on agents. This contribution is coupled with a convolutional neural network (CNN) that extracts semantic information. Semantics provide guidance in object modeling which can reduce the geometric complexity of the environment. Pairing a parametric model with a semantic label allows agents to share the knowledge of the world with much less complexity, opening a door for multi-agent systems to perform complex tasking, and human–robot cooperation. This article takes the first step towards a generalized parametric model by limiting the geometric primitives to a planar surface and providing semantic labels when appropriate. Two novel compression algorithms for depth data and a method to independently fit planes to RGB-D data are provided, so that plane data can be used for real-time odometry estimation and mapping. Additionally, we extend maps with semantic information predicted from sparse geometries (planes) by a CNN. In experiments, the advantages of our approach in terms of computational and bandwidth resources savings are demonstrated and compared with other state-of-the-art SLAM systems.

show abstract

“…For example, DVO Odometry performs interpolations for depth images, intensity images, depth gradients in

directions, and intensity gradients in

, to establish the likelihood that a given hypothesized plane image pair can be aligned with the intent to piece together large geometric map regions in a manner similar to puzzle-solving.…”

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Low-Bandwidth and Compute-Bound RGB-D Planar Semantic SLAM

Zhang

Ganesh

Volle

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Many system entities need to collectively coordinate with each other to make decisions online and collaboratively in this paradigm. Some examples of multi-agent robots are simultaneous localization and mapping (SLAM) [143], [144], [145], warehouse robotics [146], [147], [148], surgical robotics [149], [150], autonomous driving [151], [152], [153], agricultural robotics [154], [155], [156] etc. In these applications, each of the agents in a multi-agent system may be equipped with sensors like LiDAR, RGB and IR cameras, GPS receiver etc., which enable them to function autonomously.…”

Section: E Multi-agent Robotics Applicationsmentioning

confidence: 99%

Going Beyond RF: How AI-enabled Multimodal Beamforming will Shape the NextG Standard

Roy¹,

Salehi²,

Banou³

et al. 2022

Preprint

View full text Add to dashboard Cite

Incorporating artificial intelligence and machine learning (AI/ML) methods within the 5G wireless standard promises autonomous network behavior and ultra-low-latency reconfiguration. However, the effort so far has purely focused on learning from radio frequency (RF) signals. Future standards and next-generation (nextG) networks beyond 5G will have two significant evolutions over the state-of-the-art 5G implementations: (i) massive number of antenna elements, scaling up to hundreds-tothousands in number, and (ii) inclusion of AI/ML in the critical path of the network reconfiguration process that can access sensor feeds from a variety of RF and non-RF sources. While the former allows unprecedented flexibility in 'beamforming', where signals combine constructively at a target receiver, the latter enables the network with enhanced situation awareness not captured by a single and isolated data modality. This survey presents a thorough analysis of the different approaches used for beamforming today, focusing on mmWave bands, and then proceeds to make a compelling case for considering non-RF sensor data from multiple modalities, such as LiDAR, Radar, GPS for increasing beamforming directional accuracy and reducing processing time. This so called idea of multimodal beamforming will require deep learning based fusion techniques, which will serve to augment the current RF-only and classical signal processing methods that do not scale well for massive antenna arrays. The survey describes relevant deep learning architectures for multimodal beamforming, identifies computational challenges and the role of edge computing in this process, dataset generation tools, and finally, lists open challenges that the community should tackle to realize this transformative vision of the future of beamforming.

show abstract

“…A single node in a wireless sensor network often has limited energy, processing, and storage resources, and the wireless transmission of data is the most energy-intensive operation performed by the node while processing sensor data exhibits relatively low energy demands [ 8 ]. Even for networked systems that do not use wireless data transmission or that have sufficient energy resources, communication can be a limiting factor when nodes need to transmit large-scale estimates, which may occur in cooperative map building [ 9 ], cooperative localization [ 10 , 11 ], or multi-object tracking [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Conservative Quantization of Covariance Matrices with Applications to Decentralized Information Fusion

Funk

Noack

Hanebeck

2021

Sensors

View full text Add to dashboard Cite

Information fusion in networked systems poses challenges with respect to both theory and implementation. Limited available bandwidth can become a bottleneck when high-dimensional estimates and associated error covariance matrices need to be transmitted. Compression of estimates and covariance matrices can endanger desirable properties like unbiasedness and may lead to unreliable fusion results. In this work, quantization methods for estimates and covariance matrices are presented and their usage with the optimal fusion formulas and covariance intersection is demonstrated. The proposed quantization methods significantly reduce the bandwidth required for data transmission while retaining unbiasedness and conservativeness of the considered fusion methods. Their performance is evaluated using simulations, showing their effectiveness even in the case of substantial data reduction.

show abstract

Maplets: An Efficient Approach for Cooperative SLAM Map Building Under Communication and Computation Constraints

Cited by 3 publications

References 21 publications

Low-Bandwidth and Compute-Bound RGB-D Planar Semantic SLAM

Low-Bandwidth and Compute-Bound RGB-D Planar Semantic SLAM

Going Beyond RF: How AI-enabled Multimodal Beamforming will Shape the NextG Standard

Conservative Quantization of Covariance Matrices with Applications to Decentralized Information Fusion

Contact Info

Product

Resources

About