Semantically Assisted Loop Closure in SLAM Using NDT Histograms

Zaganidis, Anestis; Zerntev, Alexandros; Duckett, Tom; Cielniak, Grzegorz

doi:10.1109/iros40897.2019.8968140

Cited by 36 publications

(14 citation statements)

References 20 publications

(31 reference statements)

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“…They use a convolutional neural network to extract compact descriptors from LiDAR scans and use the features to retrieve near-by place candidates from a map and to estimate the yaw discrepancy. Most recently, Zaganidis et al (2019) proposed a normal distributions transform histogrambased loop closure detection method, which is assisted by semantic information. Kong et al (2020) also use semantic graphs for place recognition for 3D point clouds.…”

Section: Loop Closing For Slammentioning

confidence: 99%

OverlapNet: a siamese network for computing LiDAR scan similarity with applications to loop closing and localization

et al. 2021

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Localization and mapping are key capabilities of autonomous systems. In this paper, we propose a modified Siamese network to estimate the similarity between pairs of LiDAR scans recorded by autonomous cars. This can be used to address both, loop closing for SLAM and global localization. Our approach utilizes a deep neural network exploiting different cues generated from LiDAR data. It estimates the similarity between pairs of scans using the concept of image overlap generalized to range images and furthermore provides a relative yaw angle estimate. Based on such predictions, our method is able to detect loop closures in a SLAM system or to globally localize in a given map. For loop closure detection, we use the overlap prediction as the similarity measurement to find loop closure candidates and integrate the candidate selection into an existing SLAM system to improve the mapping performance. For global localization, we propose a novel observation model using the predictions provided by OverlapNet and integrate it into a Monte-Carlo localization framework. We evaluate our approach on multiple datasets collected using different LiDAR scanners in various environments. The experimental results show that our method can effectively detect loop closures surpassing the detection performance of state-of-the-art methods and that it generalizes well to different environments. Furthermore, our method reliably localizes a vehicle in typical urban environments globally using LiDAR data collected in different seasons.

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Section: Loop Closing For Slammentioning

confidence: 99%

OverlapNet: a siamese network for computing LiDAR scan similarity with applications to loop closing and localization

et al. 2021

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“…To improve the matching time and detection accuracy of histogram-based loop detection methods, Zaganidis et al [ 102 ] generated an NDT histogram-based local descriptor using semantic information obtained from PointNet++ [ 103 ]. Here, [ 104 ] implemented PointNetVLAD [ 105 ] which integrates PointNet [ 106 ] and NetVLAD [ 107 ] to generate a global descriptor from 3D point cloud.…”

Section: Role Of Deep Learning In Loop Closure Detectionmentioning

confidence: 99%

Role of Deep Learning in Loop Closure Detection for Visual and Lidar SLAM: A Survey

Arshad

Kim

2021

Sensors

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Loop closure detection is of vital importance in the process of simultaneous localization and mapping (SLAM), as it helps to reduce the cumulative error of the robot’s estimated pose and generate a consistent global map. Many variations of this problem have been considered in the past and the existing methods differ in the acquisition approach of query and reference views, the choice of scene representation, and associated matching strategy. Contributions of this survey are many-fold. It provides a thorough study of existing literature on loop closure detection algorithms for visual and Lidar SLAM and discusses their insight along with their limitations. It presents a taxonomy of state-of-the-art deep learning-based loop detection algorithms with detailed comparison metrics. Also, the major challenges of conventional approaches are identified. Based on those challenges, deep learning-based methods were reviewed where the identified challenges are tackled focusing on the methods providing long-term autonomy in various conditions such as changing weather, light, seasons, viewpoint, and occlusion due to the presence of mobile objects. Furthermore, open challenges and future directions were also discussed.

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“…Recently, Zaganidis et al [36] proposed a Normal Distributions Transform (NDT) histogram-based loop closure detection method, which is also assisted by semantic information. In contrast to ours, their method needs a dense global map and cannot estimate the relative yaw angle.…”

Section: Introductionmentioning

confidence: 99%

OverlapNet: Loop Closing for LiDAR-based SLAM

Chen¹,

Läbe²,

Milioto³

et al. 2020

Robotics: Science and Systems XVI

176

123

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Simultaneous localization and mapping (SLAM) is a fundamental capability required by most autonomous systems. In this paper, we address the problem of loop closing for SLAM based on 3D laser scans recorded by autonomous cars. Our approach utilizes a deep neural network exploiting different cues generated from LiDAR data for finding loop closures. It estimates an image overlap generalized to range images and provides a relative yaw angle estimate between pairs of scans. Based on such predictions, we tackle loop closure detection and integrate our approach into an existing SLAM system to improve its mapping results. We evaluate our approach on sequences of the KITTI odometry benchmark and the Ford campus dataset. We show that our method can effectively detect loop closures surpassing the detection performance of state-of-the-art methods. To highlight the generalization capabilities of our approach, we evaluate our model on the Ford campus dataset while using only KITTI for training. The experiments show that the learned representation is able to provide reliable loop closure candidates, also in unseen environments.

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Semantically Assisted Loop Closure in SLAM Using NDT Histograms

Cited by 36 publications

References 20 publications

OverlapNet: a siamese network for computing LiDAR scan similarity with applications to loop closing and localization

OverlapNet: a siamese network for computing LiDAR scan similarity with applications to loop closing and localization

Role of Deep Learning in Loop Closure Detection for Visual and Lidar SLAM: A Survey

OverlapNet: Loop Closing for LiDAR-based SLAM

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