Robust Localization in Map Changing Environments Based on Hierarchical Approach of Sliding Window Optimization and Filtering

Cho, Sungjin; Kim, Chansoo; Sunwoo, Myoungho; Jo, Kichun

doi:10.1109/tits.2020.3035801

Cited by 14 publications

(5 citation statements)

References 21 publications

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“…A practical complication with the definition of the hypothesis score in (11) is that all likelihoods that are to be compared has to be started at the same point in time for the normalizing factor to cancel. If an evaluation of a landmark is ongoing and an observation is made that is not gated with any of the already evaluated hypotheses.…”

Section: B Back-trackingmentioning

confidence: 99%

“…The real world is both dynamic and nonrigid due to movements and deformability of objects. The problem of building life-long maps that can take changes into account is often mentioned as an important remaining challenge in this field [1,5], and recently various aspect of this issue have been addressed within the automated vehicle community [6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Hypothesis SLAM for Non-Static Environments With Reoccurring Landmarks

Nielsen

Hendeby

2023

IEEE Trans. Intell. Veh.

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A static world assumption is often used when considering the simultaneous localization and mapping (SLAM) problem. In reality, especially when long-term autonomy is the objective, this is not valid. This paper studies a scenario where uniquely identifiable landmarks can attend multiple discrete positions, not known a priori. Based on a feature based multi-hypothesis map representation, a multi-hypothesis SLAM algorithm is developed inspired by target tracking theory. The creation of such a map is merged into the SLAM framework allowing any available SLAM method to solve the underlying mapping and localization problem for each hypothesis. A recursively updated hypothesis score allows for hypothesis rejection and prevents exponential growth in the number of hypotheses. The developed method is evaluated in an underground mine application, where physical barriers can be moved in between multiple distinct positions. Simulations are conducted in this environment showing the benefits of the multi-hypothesis approach compared to executing a standard SLAM algorithm. Practical considerations as well as suitable approximations are elaborated upon and experiments on real data further validates the simulated results and show that the multi-hypothesis approach has similar performance in reality as in simulation.

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Section: B Back-trackingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Hypothesis SLAM for Non-Static Environments With Reoccurring Landmarks

Nielsen

Hendeby

2023

IEEE Trans. Intell. Veh.

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“…Regarding changing environments, Berrio et al [26] detected discrepancies between maps and surroundings based on geometric relationships between vehicle posture and transient features, continuously updating the map to ensure consistency. Cho et al [6] introduced a layered approach integrating sliding window optimization and filtering for robust positioning in changing environments. Beyond specific scenarios, some scholars proposed universal fault diagnosis frameworks to ensure the performance of fusion positioning systems.…”

Section: Related Workmentioning

confidence: 99%

“…To address the issue of localization degradation in mapmatching, some studies employ approaches such as increasing the matching rate [6] or conducting random analysis with multiple-source inputs [7], but these methods introduce additional computational burden. Other research utilizes residual tests [8,9] to identify the degradation of map matching and then isolates the degraded information.…”

Section: Introductionmentioning

confidence: 99%

Degradation state detection and local map optimization for enhancing the SOTIF of map-matching-based fusion localization system

Cao,

Luo,

Wang

et al. 2023

Meas. Sci. Technol.

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In scenarios involving local map missing and local environmental changes, map-matching localization systems (MMLS) may experience degradation in localization due to the deficiency of texture and geometrical features. This consequently leads to a decrease in localization accuracy or divergence in a map-matching-based fusion localization system (MMBFLS). To address this issue and ensure the safety of the intended functionality of autonomous vehicles, this study proposes a degradation state detection and local map optimization-based fusion localization frame. To detect the degradation state of pose estimation in MMLS, the analysis of the mathematical mechanisms underlying the localization degradation in MMLS is conducted first. Subsequently, a degradation state detection method based on incremental matrix eigenvalue analysis is proposed, which leverages the magnitude of individual eigenvalues of the incremental matrix to determine the corresponding degradation state and isolates it accordingly. Furthermore, we employ sliding window-based factor graph optimization to maintain a local map that matches Light detection and ranging point clouds, compensating for the degraded states and thereby ensuring the localization performance of MMBFLS. Real-world sensor data is collected to validate the proposed method. The results indicate that the accuracy of the proposed degradation detection method reached as high as 94.67%. Moreover, the proposed localization framework can effectively ensure the localization accuracy of MMBFLS in typical highway scenarios.

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“…For detecting map changes in the GND map, the exact pose of the vehicle is very important. In order to estimate the vehicle pose in changing environments, we applied a hierarchical algorithm [44,55]. In the first process of the hierarchical architecture, a submap, which models the present environments in real-time, is constructed based on the Graph SLAM algorithm.…”

Section: Robust Localization In Map-changing Environmentsmentioning

confidence: 99%

Cloud Update of Geodetic Normal Distribution Map Based on Crowd-Sourcing Detection against Road Environment Changes

Kim

Cho²,

Sunwoo

et al. 2022

Journal of Advanced Transportation

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LiDAR-based localization has been widely used for the pose estimation of autonomous vehicles. Since the localization requires a sustainable map reflecting environment changes, a map update framework based on crowd-sourcing measurements has been researched. Unfortunately, a point cloud map occupies too large data size to transmit data in the uploading and downloading of the map update framework. To realize the LiDAR map update framework by reducing the data size, we proposed a novel map update framework using a Geodetic Normal Distribution (GND) map that compresses the point cloud to the normal distributions. The proposed GND map update framework comprises two parts: map change detection based on crowd-sourcing vehicles and map updating based on a map cloud server. GND map changes are detected based on an evidence theory considering geometric relationships between the GND map and crowd-sourcing measurements and uploaded to the map cloud server. Uploaded map changes reproduce representative map changes based on a similarity-based clustering, which are updated into the GND map. The proposed framework was evaluated in simulations and real environments on construction sites. As a result, although partial map changes occurred, the GND map was kept up-to-date through the proposed framework and the localization for autonomous driving was performed successfully.

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Robust Localization in Map Changing Environments Based on Hierarchical Approach of Sliding Window Optimization and Filtering

Cited by 14 publications

References 21 publications

Multi-Hypothesis SLAM for Non-Static Environments With Reoccurring Landmarks

Multi-Hypothesis SLAM for Non-Static Environments With Reoccurring Landmarks

Degradation state detection and local map optimization for enhancing the SOTIF of map-matching-based fusion localization system

Cloud Update of Geodetic Normal Distribution Map Based on Crowd-Sourcing Detection against Road Environment Changes

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