MC2SLAM: Real-Time Inertial Lidar Odometry Using Two-Scan Motion Compensation

Neuhaus, Frank; Koß, Tilman; Kohnen, Robert; Paulus, Dietrich

doi:10.1007/978-3-030-12939-2_5

Cited by 44 publications

(34 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Multiple datasets have been collected with our sensor suite inside the facilities of the University of Technology Sydney. These datasets have been made publicly available 1 to demonstrate the versatility of the proposed method, we have applied IN2LAAMA to the MC2SLAM dataset [29]. This dataset has been selected because it contains timestamps for every single lidar point.…”

Section: Real-data -Localisation and Mappingmentioning

confidence: 99%

“…We have chosen the MC2SLAM dataset [29] to show the performance of the proposed approach in an outdoor environment. The data has been acquired by a Velodyne HDL-32 lidar and its built-in IMU mounted on top of a car that is driven around a University campus (sequence "campus drive" of [29]). Fig.…”

Section: Datasetmentioning

confidence: 99%

“…Map generated by IN2LAAMA in outdoor environments (MC2SLAM dataset[29], sequence "campus drive") without (a) and with (b) loop closure.…”

mentioning

confidence: 99%

See 2 more Smart Citations

IN2LAMA: INertial Lidar Localisation And MApping

Gentil

Vidal-Calleja

Huang

2019

2019 International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

In this paper, we present INertial Lidar Localisation Autocalibration And MApping (IN2LAAMA): a probabilistic framework for localisation, mapping, and extrinsic calibration based on a 3D-lidar and a 6-DoF-IMU. Most of today's lidars collect geometric information about the surrounding environment by sweeping lasers across their field of view. Consequently, 3Dpoints in one lidar scan are acquired at different timestamps. If the sensor trajectory is not accurately known, the scans are affected by the phenomenon known as motion distortion. The proposed method leverages preintegration with a continuous representation of the inertial measurements to characterise the system's motion at any point in time. It enables precise correction of the motion distortion without relying on any explicit motion model. The system's pose, velocity, biases, and time-shift are estimated via a full batch optimisation that includes automatically generated loop-closure constraints. The autcalibration and the registration of lidar data relies on planar and edge features matched across pairs of scans. The performance of the framework is validated through simulated and real-data experiments.

show abstract

Section: Real-data -Localisation and Mappingmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

See 1 more Smart Citation

IN2LAMA: INertial Lidar Localisation And MApping

Gentil

Vidal-Calleja

Huang

2019

2019 International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

show abstract

“…Reflectance intensity images with depth are reconstructed from range scans in [15] and then used as an input for the visual odometry algorithm. Contrary to that, many works [16], [17], [18], [19] rely solely on the geometric properties of registered laser point clouds.…”

Section: Introductionmentioning

confidence: 99%

Sensor Aware Lidar Odometry

Kovalenko

Korobkin

Minin

2019

2019 European Conference on Mobile Robots (ECMR)

View full text Add to dashboard Cite

A lidar odometry method, integrating into the computation the knowledge about the physics of the sensor, is proposed. A model of measurement error enables higher precision in estimation of the point normal covariance. Adjacent laser beams are used in an outlier correspondence rejection scheme. The method is ranked in the KITTI's leaderboard with 1.37% positioning error. 3.67% is achieved in comparison with the LOAM method on the internal dataset.

show abstract

“…However, these algorithms are designed to build the 2D grid map for robot navigation. The 3D SLAM using the multi-beam LiDAR(MBL) remains an active research in recent years (Behley and Stachniss, 2018;Droeschel and Behnke, 2018;Neuhaus et al, 2018;Shan and Englot, 2018). Most LiDAR-SLAM approaches are variations of the traditional scan matching based on iterative closest point(ICP) (Deschaud, 2018).…”

Section: Introductionmentioning

confidence: 99%

Low-Cost Wheeled Robot-Borne Laser Scanning System for Indoor and Outdoor 3d Mapping Application

Chen

Cong

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

<p><strong>Abstract.</strong> Aiming to accomplish automatic and real-time three-dimensional mapping in both indoor and outdoor scenes, a low-cost wheeled robot-borne laser scanning system is proposed in this paper. The system includes a laser scanner, an inertial measurement unit, a modified turtlebot3 two-wheel differential chassis and etc. To achieve a globally consistent map, the system performs global trajectory optimization after detecting the loop closure. Experiments are undertaken in two typical indoor/outdoor scenes that is an underground car park and a road environment in the campus of Wuhan University. The point clouds acquired by the proposed system are quantitatively evaluated by comparing the derived point clouds with the ground truth data collected by a RIEGL VZ 400 laser scanner. The results present an accuracy of 90% points below 0.1 meter error in the tested scene, showing that its applicability and potential in indoor and mapping applications.</p>

show abstract

MC2SLAM: Real-Time Inertial Lidar Odometry Using Two-Scan Motion Compensation

Cited by 44 publications

References 20 publications

IN2LAMA: INertial Lidar Localisation And MApping

IN2LAMA: INertial Lidar Localisation And MApping

Sensor Aware Lidar Odometry

Low-Cost Wheeled Robot-Borne Laser Scanning System for Indoor and Outdoor 3d Mapping Application

Contact Info

Product

Resources

About