Lidar SLAM Comparison in a Featureless Tunnel Environment

Filip, Iulian; Pyo, Juhyun; Lee, Meungsuk; Joe, Hangil

doi:10.23919/iccas55662.2022.10003820

Cited by 9 publications

(6 citation statements)

References 13 publications

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“…The SLAM method exists in various types depending on the robot form and the sensor. Furthermore, SLAM can be categorized into lidar SLAM [13][14][15][16][17][18], visual SLAM [19][20][21][22][23], and others based on the type of sensor, and can also be divided into feature-based SLAM [15,16], fraph-based SLAM [17,18], etc., based on the method of data processing.…”

Section: Related Work 21 Simultaneous Localization and Mappingmentioning

confidence: 99%

“…However, it does not address the need for a closed loop, which is a feature of LOAM SLAM. The HDL Graph SLAM method [17,18] is another example of 3D lidar SLAM that uses the normal distribution transformation scan matching method to estimate the robot's driving distance and loop detection in graph-based SLAM.…”

Section: Related Work 21 Simultaneous Localization and Mappingmentioning

confidence: 99%

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A Drone’s 3D Localization and Load Mapping Based on QR Codes for Load Management

Kang,

Jung

2024

Drones

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The ongoing expansion of the Fourth Industrial Revolution has led to a diversification of drone applications. Among them, this paper focuses on the critical technology required for load management using drones. Generally, when using autonomous drones, global positioning system (GPS) receivers attached to the drones are used to determine the drone’s position. However, GPS integrated into commercially available drones have an error margin on the order of several meters. This paper, proposes a method that uses fixed-size quick response (QR) codes to maintain the error of drone 3D localization within a specific range and enable accurate mapping. In the drone’s 3D localization experiment, the errors were maintained within a specific range, with average errors ranging from approximately 0 to 3 cm, showing minimal differences. During the mapping experiment, the average error between the actual and estimated positions of the QR codes was consistently around 0 to 3 cm.

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Section: Related Work 21 Simultaneous Localization and Mappingmentioning

confidence: 99%

Section: Related Work 21 Simultaneous Localization and Mappingmentioning

confidence: 99%

A Drone’s 3D Localization and Load Mapping Based on QR Codes for Load Management

Kang,

Jung

2024

Drones

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“…Before conducting the work described in this paper, we used the same robot platform to compare seven state-of-the-art LiDAR SLAM algorithms in the tunnel environment [19]. The compared LiDAR SLAM algorithms were as follows: LeGO-LOAM [9], LIO-SAM [10], F-LOAM [11], HDL-SLAM [12], FAST-LIO2 [13], SC-LeGO-LOAM [20], and SC-LIO-SAM [21].…”

Section: Related Workmentioning

confidence: 99%

“…Similar to LeGO-LOAM, FAST-LIO2 is a computationally inexpensive algorithm, causing it to be suitable for usage on single-board computers. Additionally, by using the wheel encoder data from AGV, we plan to improve the performance of the FAST-LIO2 on the inclined terrain section of the tunnel environment because FAST-LIO2 outputs high error results in our preliminary experiment on the inclined terrain [19].…”

Section: Related Workmentioning

confidence: 99%

LiDAR SLAM with a Wheel Encoder in a Featureless Tunnel Environment

et al. 2023

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Simultaneous localization and mapping (SLAM) represents a crucial algorithm in the autonomous navigation of ground vehicles. Several studies were conducted to improve the SLAM algorithm using various sensors and robot platforms. However, only a few works have focused on applications inside low-illuminated featureless tunnel environments. In this work, we present an improved SLAM algorithm using wheel encoder data from an autonomous ground vehicle (AGV) to obtain robust performance in a featureless tunnel environment. The improved SLAM system uses FAST-LIO2 LiDAR SLAM as the baseline algorithm, and the additional wheel encoder sensor data are integrated into the baseline SLAM structure using the extended Kalman filter (EKF) algorithm. The EKF algorithm is used after the LiDAR odometry estimation and before the mapping process of FAST-LIO2. The prediction step uses the wheel encoder and inertial measurement unit (IMU) data, while the correction step uses the FAST-LIO2 LiDAR state estimation. We used an AGV to conduct experiments in flat and inclined terrain sections in a tunnel environment. The results showed that the mapping and the localization process in the SLAM algorithm was greatly improved in a featureless tunnel environment considering both inclined and flat terrains.

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“…However, in outdoor areas, those systems suffer from overexposure and limited distance. Another option is moving the system on a controlled path, as carried out by Filip et al [31]. They test different state-of-the-art SLAM algorithms in a featureless tunnel.…”

Section: Introductionmentioning

confidence: 99%

Automated Method for SLAM Evaluation in GNSS-Denied Areas

Merkle,

Reiterer

2023

Remote Sensing

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The automated inspection and mapping of engineering structures are mainly based on photogrammetry and laser scanning. Mobile robotic platforms like unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), but also handheld platforms, allow efficient automated mapping. Engineering structures like bridges shadow global navigation satellite system (GNSS), which complicates precise localization. Simultaneous localization and mapping (SLAM) algorithms offer a sufficient solution, since they do not require GNSS. However, testing and comparing SLAM algorithms in GNSS-denied areas is difficult due to missing ground truth data. This work presents an approach to measuring the performance of SLAM in indoor and outdoor GNSS-denied areas using a terrestrial scanner Leica RTC360 and a tachymeter to acquire point cloud and trajectory information. The proposed method is independent of time synchronization between robot and tachymeter and also works on sparse SLAM point clouds. For the evaluation of the proposed method, three LiDAR-based SLAM algorithms called KISS-ICP, SC-LIO-SAM, and MA-LIO are tested using a UGV equipped with two light detection and ranging (LiDAR) sensors and an inertial measurement unit (IMU). KISS-ICP is based solely on a single LiDAR scanner and SC-LIO-SAM also uses an IMU. MA-LIO, which allows multiple (different) LiDAR sensors, is tested on a horizontal and vertical one and an IMU. Time synchronization between the tachymeter and SLAM data during post-processing allows calculating the root mean square (RMS) absolute trajectory error, mean relative trajectory error, and the mean point cloud to reference point cloud distance. It shows that the proposed method is an efficient approach to measure the performance of SLAM in GNSS-denied areas. Additionally, the method shows the superior performance of MA-LIO in four of six test tracks with 5 to 7 cm RMS trajectory error, followed by SC-LIO-SAM and KISS-ICP in last place. SC-LIO-SAM reaches the lowest point cloud to reference point cloud distance in four of six test tracks, with 4 to 12 cm.

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Lidar SLAM Comparison in a Featureless Tunnel Environment

Cited by 9 publications

References 13 publications

A Drone’s 3D Localization and Load Mapping Based on QR Codes for Load Management

A Drone’s 3D Localization and Load Mapping Based on QR Codes for Load Management

LiDAR SLAM with a Wheel Encoder in a Featureless Tunnel Environment

Automated Method for SLAM Evaluation in GNSS-Denied Areas

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