Real-Time Vehicle Positioning and Mapping Using Graph Optimization

Das, Anweshan; Elfring, Jos; Dubbelman, Gijs

doi:10.3390/s21082815

Cited by 10 publications

(4 citation statements)

References 38 publications

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“…Nevertheless, the robots performing long tasks are prone to perceptual aliasing and generate incorrect loop closures, posing severe challenges for graph SLAM sensitive to outliers. Regarding the susceptibility of graph SLAM to wrong loop closures, many works try to establish a robust back-end optimizer to detect and filter outliers introduced by the front-end algorithms [14,15]. Realizing, reversing, and recovering algorithm [16] can realize that the appearance-based place recognition system has generated wrong constraints, remove them if required, and re-optimize the state estimation.…”

Section: Related Workmentioning

confidence: 99%

Fast Loop Closure Selection Method with Spatiotemporal Consistency for Multi-Robot Map Fusion

2022

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This paper presents a robust method based on graph topology to find the topologically correct and consistent subset of inter-robot relative pose measurements for multi-robot map fusion. However, the absence of good prior on relative pose gives a severe challenge to distinguish the inliers and outliers, and once the wrong inter-robot loop closures are used to optimize the pose graph, which can seriously corrupt the fused global map. Existing works mainly rely on the consistency of spatial dimension to select inter-robot measurements, while it does not always hold. In this paper, we propose a fast inter-robot loop closure selection method that integrates the consistency and topology relationship of inter-robot measurements, which both conform to the continuity characteristics of similar scenes and spatiotemporal consistency. Firstly, a clustering method integrating topology correctness of inter-robot loop closures is proposed to split the entire measurement set into multiple clusters. Then, our method decomposes the traditional high-dimensional consistency matrix into the sub-matrix blocks corresponding to the overlapping trajectory regions. Finally, we define the weight function to find the topologically correct and consistent subset with the maximum cardinality, then convert the weight function to the maximum clique problem from graph theory and solve it. We evaluate the performance of our method in a simulation and in a real-world experiment. Compared to state-of-the-art methods, the results show that our method can achieve competitive performance in accuracy while reducing computation time by 75%.

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Section: Related Workmentioning

confidence: 99%

Fast Loop Closure Selection Method with Spatiotemporal Consistency for Multi-Robot Map Fusion

2022

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“…Its constraint factors included inertial factors, visual factors, code pseud-orange and Doppler factors [42]. Similarly, Das et al modeled multiple optimization graphs using visual information from a precise stereo camera-based visual odometry, inertial information from a vehicle velocity and yaw-rate sensor-based odometry, and GNSS information [43]. The FGO-NDT method reduced the drift errors of systems by using a factor graph, which combined the GNSS location and loop information [44].…”

Section: Graph Optimization-based Map Constructionmentioning

confidence: 99%

Crowdsourcing-Based Indoor Semantic Map Construction and Localization Using Graph Optimization

Chai

Yang

et al. 2022

Sensors

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The advancement of smartphones with multiple built-in sensors facilitates the development of crowdsourcing-based indoor map construction and localization. This paper proposes a crowdsourcing-based indoor semantic map construction and localization method using graph optimization. Using waypoints, semantic landmarks, and Wi-Fi landmarks as nodes and the relevance between waypoints and landmarks (i.e., waypoint–waypoint, waypoint–semantic, waypoint–Wi-Fi, semantic–semantic, and Wi-Fi–Wi-Fi) as edges, the optimization graph is constructed. Initializing the venue map is the single-track semantic map with the highest quality, as determined by a proposed map quality evaluation function. The aligned venue and candidate maps are optimized while satisfying the constraints, with the candidate map exhibiting the highest degree of similarity to the venue map. The lightweight venue map is then updated in terms of waypoint and landmark attributes, as well as the relationship between waypoints and landmarks. To determine a pedestrian’s location on a venue map, similarities between a local map and a venue map are evaluated. Experiments conducted in an office building and shopping mall scenes demonstrate that crowdsourcing-based venue maps are superior to single-track semantic maps. Additionally, the landmark matching-based localization method can achieve a mean localization error of less than 0.5 m on the venue map, compared to 0.6 m in a single-track semantic map.

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“…In order to solve this problem, in this paper, we first perform the outlier rejection process on the acquired IPS information before performing the positional fusion. Inspired by the adaptive GNSS outlier rejection algorithm proposed in Das [ 36 ], this section proposes an outlier suppression algorithm for the IPS positioning module to pre-process the IPS data before fusion and eliminate the influence of outliers on the system.…”

Section: Back-end Optimization Algorithm For Visual Fusion Ipsmentioning

confidence: 99%

SLAM Back-End Optimization Algorithm Based on Vision Fusion IPS

Xia

Cheng

Cai

et al. 2022

Sensors

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SLAM (Simultaneous Localization and Mapping) is mainly composed of five parts: sensor data reading, front-end visual odometry, back-end optimization, loopback detection, and map building. And when visual SLAM is estimated by visual odometry only, cumulative drift will inevitably occur. Loopback detection is used in classical visual SLAM, and if loopback is not detected during operation, it is not possible to correct the positional trajectory using loopback. Therefore, to address the cumulative drift problem of visual SLAM, this paper adds Indoor Positioning System (IPS) to the back-end optimization of visual SLAM, and uses the two-label orientation method to estimate the heading angle of the mobile robot as the pose information, and outputs the pose information with position and heading angle. It is also added to the optimization as an absolute constraint. Global constraints are provided for the optimization of the positional trajectory. We conducted experiments on the AUTOLABOR mobile robot, and the experimental results show that the localization accuracy of the SLAM back-end optimization algorithm with fused IPS can be maintained between 0.02 m and 0.03 m, which meets the requirements of indoor localization, and there is no cumulative drift problem when there is no loopback detection, which solves the problem of cumulative drift of the visual SLAM system to some extent.

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Real-Time Vehicle Positioning and Mapping Using Graph Optimization

Cited by 10 publications

References 38 publications

Fast Loop Closure Selection Method with Spatiotemporal Consistency for Multi-Robot Map Fusion

Fast Loop Closure Selection Method with Spatiotemporal Consistency for Multi-Robot Map Fusion

Crowdsourcing-Based Indoor Semantic Map Construction and Localization Using Graph Optimization

SLAM Back-End Optimization Algorithm Based on Vision Fusion IPS

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