MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square

  title={MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square},
  author={Yue Pan and Pengchuan Xiao and Yujie He and Zhenlei Shao and Zesong Li},
  journal={2021 IEEE International Conference on Robotics and Automation (ICRA)},
  • Yue PanPengchuan Xiao Zesong Li
  • Published 7 February 2021
  • Environmental Science
  • 2021 IEEE International Conference on Robotics and Automation (ICRA)
The rapid development of autonomous driving and mobile mapping calls for off-the-shelf LiDAR SLAM solutions that are adaptive to LiDARs of different specifications on various complex scenarios. To this end, we propose MULLS, an efficient, low-drift, and versatile 3D LiDAR SLAM system. For the front-end, roughly classified feature points (ground, facade, pillar, beam, etc.) are extracted from each frame using dual-threshold ground filtering and principal components analysis. Then the… 

Figures and Tables from this paper

Lidar-only 3D SLAM System Comparative Study

Five state-of-the-art open-source 3D lidar-only SLAM algorithms are reviewed and the experimental comparison is carried out to compare the absolute pose error (APE), efficiency, and operation memory occupation of each algorithm.

Fast and Versatile Feature-Based LiDAR Odometry via Efficient Local Quadratic Surface Approximation

We present a fast and versatile feature-based LiDAR odometry method using local quadratic surface approximation and point-to-surface alignment. Unlike most feature-based methods, our approach

KISS-ICP: In Defense of Point-to-Point ICP – Simple, Accurate, and Robust Registration If Done the Right Way

This work removes a majority of parts and focuses on the core elements of the odometry estimation process to obtain a surprisingly effective system that is simple to realize and can operate under various environmental conditions using different LiDAR sensors.

Large-Scale LiDAR Consistent Mapping using Hierachical LiDAR Bundle Adjustment

—Reconstructing an accurate and consistent large- scale LiDAR point cloud map is crucial for robotics applications. The existing solution, pose graph optimization, though it is time- efficient, does

InTEn-LOAM: Intensity and Temporal Enhanced LiDAR Odometry and Mapping

Traditional LiDAR odometry (LO) systems mainly leverage geometric information obtained from the traversed surroundings to register lazer scans and estimate LiDAR ego-motion, while they may be

Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review

This paper conducts a comprehensive review of sensor modalities, including Inertial Measurement Units (IMUs), Light Detection and Ranging (LiDAR), radio detection and ranging (radar), and cameras, as well as applications of polymers in these sensors, for indoor odometry.

LiDAR-Based Real-Time Panoptic Segmentation via Spatiotemporal Sequential Data Fusion

This study proposes a spatiotemporal sequential data fusion strategy that fused points in “thing classes” based on accurate data statistics that could increase the proportion of valuable data in unbalanced datasets, and thus mitigate the adverse impact of class imbalance in the limited training data.

CT-ICP: Real-time Elastic LiDAR Odometry with Loop Closure

A new real-time LiDAR-only odometry method called CT-ICP (for Continuous-Time ICP), completed into a full SLAM with a novel loop detection procedure, which allows both the elastic distortion of the scan during the registration for increased precision, and the increased robustness to high frequency motions from the discontinuity.

Globally Consistent 3D LiDAR Mapping With GPU-Accelerated GICP Matching Cost Factors

This letter presents a real-time 3D LiDAR mapping framework based on global matching cost minimization. The proposed method constructs a factor graph that directly minimizes matching costs between

Picking up Speed: Continuous-Time Lidar-Only Odometry Using Doppler Velocity Measurements

Frequency-Modulated Continuous-Wave (FMCW) lidar is a recently emerging technology that additionally enables per-return instantaneous relative radial velocity measurements via the Doppler effect. In



S4-SLAM: A real-time 3D LIDAR SLAM system for ground/watersurface multi-scene outdoor applications

A real-time 3D LIDAR SLAM system (S4-SLAM) for unmanned vehicles/ships is proposed in this paper, which is composed of the odometry function in front-end and the loop closure function in back-end.

IMLS-SLAM: Scan-to-Model Matching Based on 3D Data

  • Jean-Emmanuel Deschaud
  • Environmental Science
    2018 IEEE International Conference on Robotics and Automation (ICRA)
  • 2018
This work presents a new low-drift SLAM algorithm based only on 3D LiDAR data that relies on a scan-to-model matching framework and uses the Implicit Moving Least Squares (IMLS) surface representation.

LOAM: Lidar Odometry and Mapping in Real-time

The method achieves both low-drift and low-computational complexity without the need for high accuracy ranging or inertial measurements and can achieve accuracy at the level of state of the art offline batch methods.

Practical optimal registration of terrestrial LiDAR scan pairs

Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments

A novel, dense approach to laserbased mapping that operates on three-dimensional point clouds obtained from rotating laser sensors is proposed that is efficient and enables real-time capable registration and is able to detect loop closures and to perform map updates in an online fashion.

LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping

We propose a framework for tightly-coupled lidar inertial odometry via smoothing and mapping, LIO-SAM, that achieves highly accurate, real-time mobile robot trajectory estimation and map-building.

LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain

  • Tixiao ShanBrendan Englot
  • Computer Science
    2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • 2018
A lightweight and ground-optimized lidar odometry and mapping method, LeGO-LOAM, for realtime six degree-of-freedom pose estimation with ground vehicles and integrated into a SLAM framework to eliminate the pose estimation error caused by drift is integrated.

SuMa++: Efficient LiDAR-based Semantic SLAM

An extension of a recently published surfel-based mapping approach exploiting three-dimensional laser range scans by integrating semantic information to facilitate the mapping process, which enables us to reliably filter moving objects, but also improve the projective scan matching via semantic constraints.

SLOAM: Semantic Lidar Odometry and Mapping for Forest Inventory

It is shown that traditional lidar and image based methods fail in the forest environment on both Unmanned Aerial Vehicle (UAV) and hand-carry systems, while this method is more robust, scalable, and automatically generates tree diameter estimations.