Scan Context++: Structural Place Recognition Robust to Rotation and Lateral Variations in Urban Environments

Kim, Giseop; Choi, Sun-Wook; Kim, Ayoung

doi:10.1109/tro.2021.3116424

Cited by 148 publications

(60 citation statements)

References 51 publications

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“…We mainly compare our method with four methods: M2DP [17], Fast Histogram [9], ScanContext [6], and ScanCon-text++ [18]. All methods are implemented using Python.…”

Section: B Comparative Methodsmentioning

confidence: 99%

“…They adopted polar transformation on the point clouds and used the height information as the feature in a polar bin. Recently, Kim et al [18] further enhanced the scan context image to achieve robustness to lateral/rotational changes.…”

Section: A Handcraft Descriptorsmentioning

confidence: 99%

“…As shown in Table I, we summarize the place recognition performance on Oxford dataset with two place density settings, 20m and 50m. Accuracy is measured at 50% recall for comparison following [18]. The evaluated values of TI-RING are almost twice as the other methods for 50m place density.…”

Section: Place Recognition Analysismentioning

confidence: 99%

See 2 more Smart Citations

One RING to Rule Them All: Radon Sinogram for Place Recognition, Orientation and Translation Estimation

Sha¹,

Xu²,

Yin³

et al. 2022

Preprint

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LiDAR-based global localization is a fundamental problem for mobile robots. It consists of two stages, place recognition and pose estimation, and yields the current orientation and translation, using only the current scan as query and a database of map scans. Inspired by the definition of a recognized place, we consider that a good global localization solution should keep the pose estimation accuracy with a lower place density. Following this idea, we propose a novel framework towards sparse place-based global localization, which utilizes a unified and learning-free representation, Radon sinogram (RING), for all sub-tasks. Based on the theoretical derivation, a translation invariant descriptor and an orientation invariant metric are proposed for place recognition, achieving certifiable robustness against arbitrary orientation and large translation between query and map scan. In addition, we also utilize the property of RING to propose a global convergent solver for both orientation and translation estimation, arriving at global localization. Evaluation of the proposed RING based framework validates the feasibility and demonstrates a superior performance even under a lower place density.

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“…We mainly compare our method with four methods: M2DP [17], Fast Histogram [9], ScanContext [6], and ScanCon-text++ [18]. All methods are implemented using Python.…”

Section: B Comparative Methodsmentioning

confidence: 99%

Section: A Handcraft Descriptorsmentioning

confidence: 99%

See 1 more Smart Citation

One RING to Rule Them All: Radon Sinogram for Place Recognition, Orientation and Translation Estimation

Sha¹,

Xu²,

Yin³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…• As opposed to conventional methods, our method is not limited to only places that are covered by LiDAR maps. [5,6,7,8,9]. GLARE transforms 2D LiDAR scans into histograms and uses the bagof-words framework [5].…”

Section: Introductionmentioning

confidence: 99%

“…M2DP projects 3D point clouds into multiple 2D planes and uses their densities as descriptors, which are more robust even with the presence of noisy point clouds [6]. The Scan context extracts the highest point for every angle and calculates the distance from the point clouds, outperforming other descriptor-based methods [8,9].…”

Section: Introductionmentioning

confidence: 99%

OpenStreetMap-based LiDAR Global Localization in Urban Environment without a Prior LiDAR Map

Cho¹,

Kim²,

Lee³

et al. 2022

Preprint

Self Cite

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Using publicly accessible maps, we propose a novel vehicle localization method that can be applied without using prior light detection and ranging (LiDAR) maps. Our method generates OSM descriptors by calculating the distances to buildings from a location in OpenStreetMap at a regular angle, and LiDAR descriptors by calculating the shortest distances to building points from the current location at a regular angle. Comparing the OSM descriptors and LiDAR descriptors yields a highly accurate vehicle localization result. Compared to methods that use prior LiDAR maps, our method presents two main advantages: (1) vehicle localization is not limited to only places with previously acquired LiDAR maps, and (2) our method is comparable to LiDAR map-based methods, and especially outperforms the other methods with respect to the top one candidate at KITTI dataset sequence 00.

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An autonomous UAV system based on adaptive LiDAR inertial odometry for practical exploration in complex environments

Kim,

Azhari,

Park

et al. 2024

Journal of Field Robotics

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Unmanned aerial vehicles (UAVs) offer many advantages over ground vehicles, including quadruped robots, based on high maneuverability when performing exploration in complex and unknown environments. However, due to their limited computational capability, UAVs require lightweight but accurate state estimation algorithms for reliable exploration. In this paper, we propose a segmented map‐based exploration system based on light detection and ranging (LiDAR)‐based state estimation for UAVs. The proposed system includes capabilities such as exploration, obstacle avoidance, and object detection with localization using three‐dimensional (3D) dense maps generated by tightly coupled LiDAR Inertial Odometry (LIO). Our proposed system is a hybrid system that can switch between guided and exploration modes, making it practical for search and rescue missions in disaster scenarios. The proposed LIO algorithm adapts to its surroundings, allowing for fast and accurate state estimation in complex environments. The proposed exploration algorithm is designed to cover specific regions in the 3D dense map generated by the proposed LIO, with the UAV determining if map points are included within the coverage area. We tested the proposed system in both simulation and real‐world environments and validated that the proposed system outperforms state‐of‐the‐art algorithms in various aspects such as localization accuracy and exploration efficiency in complex environments.

show abstract

Scan Context++: Structural Place Recognition Robust to Rotation and Lateral Variations in Urban Environments

Cited by 148 publications

References 51 publications

One RING to Rule Them All: Radon Sinogram for Place Recognition, Orientation and Translation Estimation

One RING to Rule Them All: Radon Sinogram for Place Recognition, Orientation and Translation Estimation

OpenStreetMap-based LiDAR Global Localization in Urban Environment without a Prior LiDAR Map

An autonomous UAV system based on adaptive LiDAR inertial odometry for practical exploration in complex environments

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