IN2LAMA: INertial Lidar Localisation And MApping

Gentil, Cedric Le; Vidal-Calleja, Teresa; Huang, Shoudong

doi:10.1109/icra.2019.8794429

Cited by 49 publications

(34 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, an offline batch optimisation framework is proposed here. This paper extends our previous work on lidar-inertial localisation and mapping [5]. The first key contribution with respect to our previous work is the use of IMU factors between consecutive poses and velocities of the estimated state.…”

Section: Introductionmentioning

confidence: 59%

“…This subsection of the proposed method has been described in the conference paper [5], but for completeness, it is also described here with additional details.…”

Section: A Feature Extractionmentioning

confidence: 99%

“…1) Odometry: First, we want to evaluate the advantages of using IMU factors for odometry-like localisation and mapping by comparing the accuracy of IN2LAAMA against [10], and our previous work [5]. We evaluate the localisation accuracy of the three frameworks on three sets of trajectories that have different levels of angular velocities.…”

Section: B Simulation -Localisation and Mappingmentioning

confidence: 99%

“…Table I reports the trajectories' parameters as well as the localisation errors against ground truth for [10], [5], and IN2LAAMA. The poor performance of [10] is easily explained by the fact that the motion starts at the very beginning of the simulation.…”

Section: B Simulation -Localisation and Mappingmentioning

confidence: 99%

See 3 more Smart Citations

IN2LAAMA: Inertial Lidar Localization Autocalibration and Mapping

2021

Self Cite

View full text Add to dashboard Cite

In this paper, we present INertial Lidar Localisation Autocalibration And MApping (IN2LAAMA): an offline 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, 3D-points 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 autocalibration and the registration of lidar data rely 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: Introductionmentioning

confidence: 59%

“…This subsection of the proposed method has been described in the conference paper [5], but for completeness, it is also described here with additional details.…”

Section: A Feature Extractionmentioning

confidence: 99%

Section: B Simulation -Localisation and Mappingmentioning

confidence: 99%

Section: B Simulation -Localisation and Mappingmentioning

confidence: 99%

See 2 more Smart Citations

IN2LAAMA: Inertial Lidar Localization Autocalibration and Mapping

2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…, B N , where N increases as W grows. The obstacle geometries and locations can be represented by a point cloud from laser scans and SLAM algorithms [32]. Their geometries and locations are assumed known without error in this study, the limitations from this assumption will be discussed in Section VII.…”

Section: Problem Formulationmentioning

confidence: 99%

A Scalable Sampling-Based Optimal Path Planning Approach via Search Space Reduction

Liu

2019

IEEE Access

View full text Add to dashboard Cite

Many sampling strategies in Sampling-Based Planning (SBP) often consider goal and obstacle population and may however become less efficient in large and cluttered 3D environments with a goal distanced away. This paper presents a search-space-Reduced optimal SBP approach (RSBP) for a rigid body. This reduced space is found by a sparse search tree, which is enabled by a Metric Function (MF) built on a neural network. The offline-learnt MF estimates the minimum traveling cost between any two nodes in a fixed small workspace with various obstacles. It allows connections of two sparse nodes without path planning, where the connections represent the traveling costs (not paths). It is proven that the asymptotic optimality is preserved in the RSBP (assuming a zero-error MF) and the optimality degeneration is bounded (assuming a bounded-error MF). The computational complexity during planning is shown linear to the Lebesgue measure of the entire search space (assuming the same sampling density across environments). Numerical simulations have shown that in tested large and cluttered environments the RSBP is at least as fast as the bidirectional fast marching tree* and informed rapidly exploring random tree*, with planned paths of similar optimality. The results also have shown the RSBP's improved scalability to large environments and enhanced efficiency in dealing with narrow passages. INDEX TERMS Learning, path planning.

show abstract

Point‐LIO: Robust High‐Bandwidth Light Detection and Ranging Inertial Odometry

Wei

Chen

et al. 2023

Advanced Intelligent Systems

View full text Add to dashboard Cite

Herein, point light detection and ranging inertial odometry (LIO) is presented: a robust and high‐bandwidth light detection and ranging (LiDAR) inertial odometry with the capability to estimate extremely aggressive robotic motions. Point‐LIO has two key novelties. The first one is a point‐by‐point LIO framework that updates the state at each LiDAR point measurement. This framework allows an extremely high‐frequency odometry output, significantly increases the odometry bandwidth, and fundamentally removes the artificial in‐frame motion distortion. The second one is a stochastic process‐augmented kinematic model which models the IMU measurement as an output. This new modeling method enables accurate localization and reliable mapping for aggressive motions even with inertial measurement unit (IMU) measurements saturated in the middle of the motion. Various real‐world experiments are conducted for performance evaluation. Overall, Point‐LIO is capable to provide accurate, high‐frequency odometry (4–8 kHz) and reliable mapping under severe vibrations and aggressive motions with high angular velocity (75 rad s−1) beyond the IMU measuring ranges. Furthermore, an exhaustive benchmark comparison is conducted. Point‐LIO achieves consistently comparable accuracy and time consumption. Finally, two example applications of Point‐LIO are demonstrated, one is a racing drone and the other is a self‐rotating unmanned aerial vehicle, both have aggressive motions.

show abstract

IN2LAMA: INertial Lidar Localisation And MApping

Cited by 49 publications

References 29 publications

IN2LAAMA: Inertial Lidar Localization Autocalibration and Mapping

IN2LAAMA: Inertial Lidar Localization Autocalibration and Mapping

A Scalable Sampling-Based Optimal Path Planning Approach via Search Space Reduction

Point‐LIO: Robust High‐Bandwidth Light Detection and Ranging Inertial Odometry

Contact Info

Product

Resources

About