Pairwise Consistent Measurement Set Maximization for Robust Multi-Robot Map Merging

Mangelson, Joshua G.; Dominic, Derrick; Eustice, Ryan M.; Vasudevan, Ram

doi:10.1109/icra.2018.8460217

Cited by 148 publications

(140 citation statements)

References 15 publications

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“…Latif et al [7], Carlone et al [29], Graham et al [30] look for sets of mutually consistent measurements. Mangelson et al [1] extend the latter idea to the multi-robot case and propose an effective graphtheoretic technique to find pairwise-consistent measurements among the inter-robot loop closures. Dong et al [16] adopt expectation maximization for robust multi-robot PGO.…”

Section: B Robust Pgomentioning

confidence: 99%

“…We adopt the Pairwise Consistent Measurement Set Maximization (PCM) technique proposed by Mangelson et al [1] for outlier rejection and tailor it to a fully distributed setup.…”

Section: B Distributed Robust Pgomentioning

confidence: 99%

“…4) formed by the loop closures (z αi β l ,z αj β k ) and the odometry (z αi αj ,z β k β l ) must compose to the identity, and the consistency metric (1) assesses that the noise accumulated along the cycle is consistent with the noise covariance Σ. The PCM likelihood threshold γ can be determined from the quantile of the chisquared distribution for a given probability level [1].…”

Section: B Distributed Robust Pgomentioning

confidence: 99%

“…Obtaining such a shared situational awareness is challenging since the sensor data required for SLAM is distributed across the robots, and communicating raw data may be slow (due to bandwidth constraints) or infeasible (due to limited communication range). For these reasons, current systems either rely on a centralized and offline post-processing step [1], assume all robots are always within communication range [2], or assume centralized pre-processing of the sensor data (e.g., to remove outliers [3]). We believe more flexible solutions are necessary for a broader adoption of multirobot technologies.…”

Section: Introductionmentioning

confidence: 99%

“…The first module is a pose graph optimizer that is robust to spurious measurements. We propose an implementation of distributed pose graph optimization along the lines of [3] combined with an outlier rejection mechanism based on [1], that we adapted for online and distributed operation. An example of the robustness afforded by the proposed module is showcased in Fig.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

DOOR-SLAM: Distributed, Online, and Outlier Resilient SLAM for Robotic Teams

Lajoie

Ramtoula

Chang

et al. 2020

IEEE Robot. Autom. Lett.

174

104

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To achieve collaborative tasks, robots in a team need to have a shared understanding of the environment and their location within it. Distributed Simultaneous Localization and Mapping (SLAM) offers a practical solution to localize the robots without relying on an external positioning system (e.g. GPS) and with minimal information exchange. Unfortunately, current distributed SLAM systems are vulnerable to perception outliers and therefore tend to use very conservative parameters for inter-robot place recognition. However, being too conservative comes at the cost of rejecting many valid loop closure candidates, which results in less accurate trajectory estimates. This paper introduces DOOR-SLAM, a fully distributed SLAM system with an outlier rejection mechanism that can work with less conservative parameters. DOOR-SLAM is based on peerto-peer communication and does not require full connectivity among the robots. DOOR-SLAM includes two key modules: a pose graph optimizer combined with a distributed pairwise consistent measurement set maximization algorithm to reject spurious inter-robot loop closures; and a distributed SLAM front-end that detects inter-robot loop closures without exchanging raw sensor data. The system has been evaluated in simulations, benchmarking datasets, and field experiments, including tests in GPS-denied subterranean environments. DOOR-SLAM produces more inter-robot loop closures, successfully rejects outliers, and results in accurate trajectory estimates, while requiring low communication bandwidth. Full source code is available at https://github.com/MISTLab/DOOR-SLAM.git.

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Section: B Robust Pgomentioning

confidence: 99%

“…We adopt the Pairwise Consistent Measurement Set Maximization (PCM) technique proposed by Mangelson et al [1] for outlier rejection and tailor it to a fully distributed setup.…”

Section: B Distributed Robust Pgomentioning

confidence: 99%

Section: B Distributed Robust Pgomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

DOOR-SLAM: Distributed, Online, and Outlier Resilient SLAM for Robotic Teams

Lajoie

Ramtoula

Chang

et al. 2020

IEEE Robot. Autom. Lett.

174

104

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show abstract

Experimental evaluation of Visual‐Inertial Odometry systems for arable farming

Cremona

Comelli

Pire

2022

Journal of Field Robotics

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The farming industry constantly seeks the automation of different processes involved in agricultural production, such as sowing, harvesting and weed control. The use of mobile autonomous robots to perform those tasks is of great interest. Arable lands present hard challenges for Simultaneous Localization and Mapping (SLAM) systems, key for mobile robotics, given the visual difficulty due to the highly repetitive scene and the crop leaves movement caused by the wind. In recent years, several Visual-Inertial Odometry (VIO) and SLAM systems have been developed.They have proved to be robust and capable of achieving high accuracy in indoor and outdoor urban environments. However, they were not properly assessed in agricultural fields. In this study we assess the most relevant state-of-the-art VIO systems in terms of accuracy and processing time on arable lands to better understand how they behave on these environments. In particular, the evaluation is carried out on a collection of sensor data recorded by our wheeled robot in a soybean field, which was publicly released as the Rosario data set. The evaluation shows that the highly repetitive appearance of the environment, the strong vibration produced by the rough terrain and the movement of the leaves caused by the wind, expose the limitations of the current state-of-the-art VIO and SLAM systems. We analyze the systems failures and highlight the observed drawbacks, including initialization failures, tracking loss and sensitivity to Inertial Measurement Unit saturation. Finally, we conclude that even though certain systems like ORB-SLAM3 and stereo Multi-State Constraint Kalman Filter show good results with respect to others, more improvements should be done to make them reliable in agricultural fields for certain applications such as soil tillage of crop rows and pesticide spraying.

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Search and Rescue Under the Forest Canopy Using Multiple UAS

Tian

Liu

et al. 2020

Springer Proceedings in Advanced Robotics

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We present a multi-robot system for GPS-denied search and rescue under the forest canopy. Forests are particularly challenging environments for collaborative exploration and mapping, in large part due to the existence of severe perceptual aliasing which hinders reliable loop closure detection for mutual localization and map fusion. Our proposed system features unmanned aerial vehicles (UAVs) that perform onboard sensing, estimation, and planning. When communication is available, each UAV transmits compressed tree-based submaps to a central ground station for collaborative simultaneous localization and mapping (CSLAM). To overcome high measurement noise and perceptual aliasing, we use the local configuration of a group of trees as a distinctive feature for robust loop closure detection. Furthermore, we propose a novel procedure based on cycle consistent multiway matching to recover from incorrect pairwise data associations. The returned global data association is guaranteed to be cycle consistent, and is shown to improve both precision and recall compared to the input pairwise associations. The proposed multi-UAV system is validated both in simulation and during real-world collaborative exploration missions at NASA Langley Research Center.

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Pairwise Consistent Measurement Set Maximization for Robust Multi-Robot Map Merging

Cited by 148 publications

References 15 publications

DOOR-SLAM: Distributed, Online, and Outlier Resilient SLAM for Robotic Teams

DOOR-SLAM: Distributed, Online, and Outlier Resilient SLAM for Robotic Teams

Experimental evaluation of Visual‐Inertial Odometry systems for arable farming

Search and Rescue Under the Forest Canopy Using Multiple UAS

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