LoLa-SLAM: Low-Latency LiDAR SLAM Using Continuous Scan Slicing

Karimi, Mojtaba; Oelsch, Martin; Stengel, Oliver; Babaians, Edwin; Steinbach, Eckehard

doi:10.1109/lra.2021.3060721

Cited by 42 publications

(20 citation statements)

References 32 publications

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“…Singh et al introduce a tightly coupled EKF-based VIO method, the MSCKF, which uses a filtering approach to fuse visual and inertial guidance information, extending the back-end estimation to multiple frames of information in a sliding window instead of the then common optimization of two adjacent frames [16]. Based on this, MSCKF proposes a linearization point in the fixed Jacobi matrix computation to avoid the observable direction anomaly problem and introduces an online estimation method for the external parameters of the camera-IMU system [17].…”

Section: Current Status Of Researchmentioning

confidence: 99%

[Retracted] Combined GNSS/SLAM‐Based High Accuracy Indoor Dynamic Target Localization Method

Zhang

Li³

et al. 2021

Wireless Communications and Mobile Computing

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As the indoor dynamic target localization does not detect and repair the circumferential jump value in time, which leads to large position and attitude errors and low-velocity stability, a combined Global Navigation Satellite System/Simultaneous Localization and Mapping- (GNSS/SLAM-) based high-precision indoor dynamic target localization method is proposed. The method uses Empirical Mode Decomposition (EMD) to decompose the noisy signal, obtains the noise energy as the dominant mode from the decomposed components, extracts the useful signal energy as the main dividing point, removes the high-frequency signal, constructs the low-frequency component to realize low-pass filtering and denoising, selects a suitable threshold processing function to make the high-frequency signal component retain the detailed signal effectively to realize high-frequency component denoising, detects and fixes the circumferential jump of the observed data, and detects and fixes the circumferential jump of each frequency. The indoor dynamic target positioning method is established by combining GNSS/SLAM to achieve high accuracy target positioning. The experimental results show that the position and attitude errors are small, and the velocity is stable, which indicates that the position information is closer to the dynamic target, i.e., the target positioning is more accurate. To address the problems of scale drift and frequent initialization due to environmental factors in monocular vision SLAM, an Ultra Wideband (UWB)/vision fusion localization model that takes into account the scale factor is proposed, which can give full play to the complementary characteristics between UWB and vision; solve the problems of visual initialization, scale ambiguity, and absolute spatial reference; and improve UWB localization accuracy and localization frequency as well as reducing the number of base stations. The model can reliably achieve 0.2 m accuracy in indoor environments with sparse texture or frequent light changes.

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Section: Current Status Of Researchmentioning

confidence: 99%

[Retracted] Combined GNSS/SLAM‐Based High Accuracy Indoor Dynamic Target Localization Method

Zhang

Li³

et al. 2021

Wireless Communications and Mobile Computing

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“…For spinning lidars, there are also low-latency approaches (LoLa-SLAM [31], LLOL [32]) that do not wait for a full scan but use partial scan data to perform the registration. Such acceleration is done by slicing the scans into several patterns, and the number of registration points is also much smaller than processing a full scan.…”

Section: Related Workmentioning

confidence: 99%

Faster-LIO: Lightweight Tightly Coupled Lidar-Inertial Odometry Using Parallel Sparse Incremental Voxels

Bai

Xiao²,

Chen³

et al. 2022

IEEE Robot. Autom. Lett.

144

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This letter presents an incremental voxel-based lidarinertial odometry (LIO) method for fast-tracking spinning and solid-state lidar scans. To achieve the high tracking speed, we neither use complicated tree-based structures to divide the spatial point cloud nor the strict k nearest neighbor (k-NN) queries to compute the point matching. Instead, we use the incremental voxels (iVox) as our point cloud spatial data structure, which is modified from the traditional voxels and supports incremental insertion and parallel approximated k-NN queries. We propose the linear iVox and PHC (Pseudo Hilbert Curve) iVox as two alternative underlying structures in our algorithm. The experiments show that the speed of iVox reaches 1000-2000 Hz per scan in solid-state lidars and over 200 Hz for 32 lines spinning lidars only with a modern CPU while still preserving the same level of accuracy.

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“…Karimi et al proposed a low-latency LiDAR SLAM using continuous scan slicing and concurrent matching to support real-time indoor navigation [ 17 ]. The continuous scan slicing splits point cloud data from a rotating LiDAR in a concurrent multi-threaded matching pipeline for 6D pose estimation with a high update rate and low latency.…”

Section: Related Workmentioning

confidence: 99%

An Entropy Analysis-Based Window Size Optimization Scheme for Merging LiDAR Data Frames

Kim

Jung

Hong

et al. 2022

Sensors

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LiDAR is a useful technology for gathering point cloud data from its environment and has been adapted to many applications. We use a cost-efficient LiDAR system attached to a moving object to estimate the location of the moving object using referenced linear structures. In the stationary state, the accuracy of extracting linear structures is low given the low-cost LiDAR. We propose a merging scheme for the LiDAR data frames to improve the accuracy by using the movement of the moving object. The proposed scheme tries to find the optimal window size by means of an entropy analysis. The optimal window size is determined by finding the minimum point between the entropy indicator of the ideal result and the entropy indicator of the actual result of each window size. The proposed indicator can describe the accuracy of the entire path of the moving object at each window size using a simple single value. The experimental results show that the proposed scheme can improve the linear structure extraction accuracy.

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LoLa-SLAM: Low-Latency LiDAR SLAM Using Continuous Scan Slicing

Cited by 42 publications

References 32 publications

[Retracted] Combined GNSS/SLAM‐Based High Accuracy Indoor Dynamic Target Localization Method

[Retracted] Combined GNSS/SLAM‐Based High Accuracy Indoor Dynamic Target Localization Method

Faster-LIO: Lightweight Tightly Coupled Lidar-Inertial Odometry Using Parallel Sparse Incremental Voxels

An Entropy Analysis-Based Window Size Optimization Scheme for Merging LiDAR Data Frames

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