Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems

Tian, Yulun; Chang, Yun; Arias, Fernando Herrera; Nieto-Granda, Carlos; How, Jonathan P.; Carlone, Luca

doi:10.1109/tro.2021.3137751

Cited by 77 publications

(30 citation statements)

References 105 publications

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“…Note that the network has not been trained on this specific data. The segmentation masks can be used as an input for isolating objects in, otherwise complicated to process, LiDAR scans and accurately generate geometric models with the goal of realizing landmark-based [46] or metric-semantic [48] SLAM.…”

Section: B Lidar Mappingmentioning

confidence: 99%

Challenges of SLAM in Extremely Unstructured Environments: The DLR Planetary Stereo, Solid-State LiDAR, Inertial Dataset

Giubilato

Stürzl

Wedler

et al. 2022

IEEE Robot. Autom. Lett.

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We present the DLR Planetary Stereo, Solid-State LiDAR, Inertial (S3LI) dataset, recorded on Mt. Etna, Sicily, an environment analogous to the Moon and Mars, using a handheld sensor suite with attributes suitable for implementation on a space-like mobile rover. The environment is characterized by challenging conditions regarding both the visual and structural appearance: severe visual aliasing poses significant limitations to the ability of visual SLAM systems to perform place recognition, while the absence of outstanding structural details, joined with the limited Field-of-View of the utilized Solid-State LiDAR sensor, challenges traditional LiDAR SLAM for the task of pose estimation using point clouds alone. With this data, that covers more than 4 kilometers of travel on soft volcanic slopes, we aim to: 1) provide a tool to expose limitations of state-ofthe-art SLAM systems with respect to environments, which are not present in widely available datasets and 2) motivate the development of novel localization and mapping approaches, that rely efficiently on the complementary capabilities of the two sensors. The dataset is accessible at the following url: https://rmc.dlr.de/s3li_dataset

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Section: B Lidar Mappingmentioning

confidence: 99%

Challenges of SLAM in Extremely Unstructured Environments: The DLR Planetary Stereo, Solid-State LiDAR, Inertial Dataset

Giubilato

Stürzl

Wedler

et al. 2022

IEEE Robot. Autom. Lett.

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show abstract

“…Recall the definition of A and C in (7). In particular, since A is a block-diagonal matrix, the i-th component of u (v) (corresponding to agent i) is given by,…”

Section: Appendix I Proofsmentioning

confidence: 99%

“…Note that the global Riemannian gradient with respect to y satisfies g y = N i=1 g iy where g iy grad y f i (x i , y). In addition, because of the block-diagonal structure of A in (7)…”

Section: Appendix I Proofsmentioning

confidence: 99%

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Distributed Riemannian Optimization with Lazy Communication for Collaborative Geometric Estimation

Tian¹,

Bedi²,

Koppel³

et al. 2022

Preprint

Self Cite

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We present the first distributed optimization algorithm with lazy communication for collaborative geometric estimation, the backbone of modern collaborative simultaneous localization and mapping (SLAM) and structure-from-motion (SfM) applications. Our method allows agents to cooperatively reconstruct a shared geometric model on a central server by fusing individual observations, but without the need to transmit potentially sensitive information about the agents themselves (such as their locations). Furthermore, to alleviate the burden of communication during iterative optimization, we design a set of communication triggering conditions that enable agents to selectively upload local information that are useful to global optimization. Our approach thus achieves significant communication reduction with minimal impact on optimization performance. As our main theoretical contribution, we prove that our method converges to first-order critical points with a sublinear convergence rate. Numerical evaluations on bundle adjustment problems from collaborative SLAM and SfM datasets show that our method performs competitively against existing distributed techniques, while achieving up to 78% total communication reduction.

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“…SLAM has been extensively researched by the robotics community with LiDAR and visual-based sensors [4], which exhibit good localization accuracy in general, but it is challenging to deal with featureless scenes (for example tunnels) and the algorithms often require a lot of computational resources. Due to its high accuracy and detection without line-of-sight, Ultra-WideBand (UWB) is widely used for localization [5] [6].…”

Section: Introductionmentioning

confidence: 99%

Distributed Ranging SLAM for Multiple Robots with Ultra-WideBand and Odometry Measurements

Liu¹,

Deng²,

Cao³

et al. 2022

Preprint

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To accomplish task efficiently in a multiple robots system, a problem that has to be addressed is Simultaneous Localization and Mapping (SLAM). LiDAR (Light Detection and Ranging) has been used for many SLAM solutions due to its superb accuracy, but its performance degrades in featureless environments, like tunnels or long corridors. Centralized SLAM solves the problem with a cloud server, which requires a huge amount of computational resources and lacks robustness against central node failure. To address these issues, we present a distributed SLAM solution to estimate the trajectory of a group of robots using Ultra-WideBand (UWB) ranging and odometry measurements. The proposed approach distributes the processing among the robot team and significantly mitigates the computation concern emerged from the centralized SLAM. Our solution determines the relative pose (also known as loop closure) between two robots by minimizing the UWB ranging measurements taken at different positions when the robots are in close proximity. UWB provides a good distance measure in line-of-sight conditions, but retrieving a precise pose estimation remains a challenge, due to ranging noise and unpredictable path traveled by the robot. To deal with the suspicious loop closures, we use Pairwise Consistency Maximization (PCM) to examine the quality of loop closures and perform outlier rejections. The filtered loop closures are then fused with odometry in a distributed pose graph optimization (DPGO) module to recover the full trajectory of the robot team. Extensive experiments are conducted to validate the effectiveness of the proposed approach.

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Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems

Cited by 77 publications

References 105 publications

Challenges of SLAM in Extremely Unstructured Environments: The DLR Planetary Stereo, Solid-State LiDAR, Inertial Dataset

Challenges of SLAM in Extremely Unstructured Environments: The DLR Planetary Stereo, Solid-State LiDAR, Inertial Dataset

Distributed Riemannian Optimization with Lazy Communication for Collaborative Geometric Estimation

Distributed Ranging SLAM for Multiple Robots with Ultra-WideBand and Odometry Measurements

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