A Benchmark for Multi-Modal Lidar SLAM with Ground Truth in GNSS-Denied Environments Ha Siery Li Qingqingy Yu Xianjiay Jorge Pe na Queraltay Zhuo Zou Tomi Westerlundy

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A Benchmark for Multi-Modal Lidar SLAM with

Ground Truth in GNSS-Denied Environments

Ha Sier†∗§, Li Qingqing†§, Yu Xianjia†, Jorge Pe˜

na Queralta†, Zhuo Zou∗, Tomi Westerlund†

†Turku Intelligent Embedded and Robotic Systems (TIERS) Lab, University of Turku, Finland.

∗School of Information Science and Technology, Fudan Universtiy, China

Emails: †{sierha, qingqli, xianjia.yu, jopequ, tovewe}@utu.ﬁ ∗{zhuo}@fudan.edu.cn,

Abstract—Lidar-based simultaneous localization and mapping

(SLAM) approaches have obtained considerable success in au-

tonomous robotic systems. This is in part owing to the high-

accuracy of robust SLAM algorithms and the emergence of new

and lower-cost lidar products. This study benchmarks current

state-of-the-art lidar SLAM algorithms with a multi-modal lidar

sensor setup showcasing diverse scanning modalities (spinning

and solid-state) and sensing technologies, and lidar cameras,

mounted on a mobile sensing and computing platform. We extend

our previous multi-modal multi-lidar dataset with additional

sequences and new sources of ground truth data. Speciﬁcally,

we propose a new multi-modal multi-lidar SLAM-assisted and

ICP-based sensor fusion method for generating ground truth

maps. With these maps, we then match real-time pointcloud data

using a natural distribution transform (NDT) method to obtain

the ground truth with full 6 DOF pose estimation. This novel

ground truth data leverages high-resolution spinning and solid-

state lidars. We also include new open road sequences with GNSS-

RTK data and additional indoor sequences with motion capture

(MOCAP) ground truth, complementing the previous forest

sequences with MOCAP data. We perform an analysis of the

positioning accuracy achieved with ten different SLAM algorithm

and lidar combinations. We also report the resource utilization in

four different computational platforms and a total of ﬁve settings

(Intel and Jetson ARM CPUs). Our experimental results show

that current state-of-the-art lidar SLAM algorithms perform very

differently for different types of sensors. More results, code,

and the dataset can be found at: github.com/TIERS/tiers-lidars-

dataset-enhanced.

Index Terms—Autonomous driving, LiDAR SLAM benchmark

solid-state LiDAR, SLAM

I. INTRODUCTION

Lidar sensors have been adopted as the core perception

sensor in many applications, from self-driving cars [1] to

unmanned aerial vehicles [2], including forest surveying and

industrial digital twins [3]. High resolution spinning lidars

enable a high-degree of awareness from the surrounding

environments. More dense 3D pointclouds and maps are in

creasing demand to support the next wave of ubiquitous

autonomous systems as well as more detailed digital twins

across industries. However, higher angular resolution comes at

increased cost in analog lidars requiring a higher number of

laser beams or a more compact electronics and optics solution.

New solid-state and other digital lidars are paving the way

§These authors have contributed equally to this manuscript.

(a) Ground truth map for one of the indoor sequences generated based on the proposed

approach (SLAM-assisted ICP-based prior map). This enables benchmarking of lidar

odometry and mapping algorithms in larger environments where a motion capture system

or similar is not available, with signiﬁcantly higher accuracy than GNSS/RTK solutions.

VLP-16

Horizon

L515

OS-0 OS-1

AVIA

OptiTrack

Markers

T265

GPS RTK

(b) Front view of the multi-modal data acquisition system. Next to each sensor, we

show the individual coordinate frames.

Fig. 1: Multi-modal lidar data acquisition platform and samples from maps

obtained in the different environments included in the dataset.

to cheaper and more widespread 3D lidar sensors capable of

dense environment mapping [4], [5], [6], [7].

So-called solid-state lidars overcome some of the challenges

of spinning lidars in terms of cost and resolution, but introduce

some new limitations in terms of a relatively small ﬁeld of

view (FoV) [8], [6]. Indeed, these lidars provide more sensing

range at signiﬁcantly lower cost [9]. Other limitations that

affect traditional approaches to lidar data processing include

irregular scanning patterns or increased motion blur.

Despite their increasing popularity, few works have bench-

arXiv:2210.00812v1 [cs.RO] 3 Oct 2022

marked the performance of both spinning lidar and solid-state

lidar in diverse environments, which limits the development

of more general-purpose lidar-based SLAM algorithms [9]. To

bridge the gap in the literature, we present a benchmark that

compares different modality lidars (spinning, solid-state) in

diverse environments, including indoor ofﬁces, long corridors,

halls, forests, and open roads. To allow for more accurate and

fair comparison, we introduce a new method for ground truth

generation in larger indoor spaces (see Fig. 1a). This enhanced

ground truth enables signiﬁcantly higher degree of quantitative

benchmarking and comparison with respect to our previous

work [9]. We hope for the extended dataset and ground truth

labels, as well as more detailed data, to provide a performance

reference for multi-modal lidar sensors in both structured and

unstructured environments to both academia and industry.

In summary, this work evaluates state-of-the-art SLAM

algorithms with a multi-modal multi-lidar platform as an

extension of our previous work [9]. The main contributions

of this work are as follows:

1) a ground truth trajectory generation method for envi-

ronments where MOCAP or GNSS/RTK are unavailable

that leverages the multi-modality of the data acquisition

platform and high-resolution sensors;

2) a new dataset with data from 5 different lidar sensors,

one lidar camera, and one stereo ﬁsheye cameras in a

variety of environments as illustrated in Fig. 1b. Ground

truth data is provided for all sequences;

3) the benchmarking of ten state-of-the-art ﬁlter-based and

optimization-based SLAM methods on our proposed

dataset in terms of the accuracy of odometry, memory and

computing resource consumption. The results indicate the

limitations of current SLAM algorithms and potential

future research directions.

The structure of the paper is as follows. Section II surveys

recent progress in SLAM and existing lidar-based SLAM

benchmarks. Section III provides an overview of the conﬁg-

uration of the proposed sensor system. Section IV offers the

detailed benchmark and ground truth generation methodology.

Section V concludes the study and suggests future work.

II. RELATED WORKS

Owing to high accuracy, versatility, and resilience across

environments, 3D lidar SLAM has received much study as a

crucial component of robotic and autonomous systems [10].

In this section, we limit the scope to the well-known and well-

tested 3D lidar SLAM methods. We also include an overview

of the most recent 3D lidar SLAM benchmarks.

A. 3D Lidar SLAM

The primary types of 3D lidar SLAM algorithms to-

day are lidar-only [11], and loosely-coupled [12] or tightly-

coupled [13] with IMU data. Tightly-coupled approaches

integrate the lidar and IMU data at an early stage, in opposition

to SLAM methods that loosely fuse the lidar and IMU outputs

towards the end of their respective processing pipelines.

In terms of lidar-only methods, an early work by Zhang

et al. on Lidar Odometry and Mapping (LOAM) introduced

a method that can achieve low-drift and low-computational

complexity already in 2014 [14]. Since then, there have been

multiple variations of LOAM that enhance its performance. By

incorporating a ground point segmentation and a loop closure

module, LeGO-LOAM is more lightweight with the same

accuracy but improved computational expense and lower long-

term drift [15]. However, lidar-only approaches are mainly

limited by a high susceptibility to featureless landscapes [16],

[17]. By incorporating IMU data into the state estimation

pipeline, SLAM systems naturally become more precise and

ﬂexible.

In LIOM [13], the authors proposed a novel tightly-coupled

approach with lidar-IMU fusion based on graph optimization

which outperformed the state-of-the-art lidar-only and loosely

coupled. Owing to the better performance of tightly-coupled

approaches, subsequent studies have focused in this direction.

Another practical tightly-coupled method is Fast-LIO [18],

which provides computational efﬁciency and robustness by

fusing the feature points with IMU data through a iter-

ated extended Kalman ﬁlter. By extending FAST-LIO, FAST-

LIO2 [19] integrated a dynamic structure ikd-tree to the system

that allows for the incremental map update at every step,

addressing computational scalability issues while inheriting

the tightly-coupled fusion framework from FAST-LIO.

The vast majority of these algorithms function well with

spinning lidars. Nonetheless, new approaches are in demand

since new sensors such as solid-state Livox lidars have

emerged novel sensing modalities, smaller FoVs and irregular

samplings have emerged [9]. Multiple existing studies using

enhanced SLAM algorithms are being researched to ﬁt these

new lidar characteristics. Loam livox [20] is a robust and real-

time LOAM algorithm for these types of lidars. LiLi-OM [6] is

another tightly-coupled method that jointly minimizes the cost

derived from lidar and IMU measurements for both solid-state

Lidars and conventional Lidars.

It is worth mentioning that there are other studies addressing

lidar odometry and mapping by fusing not only IMU but also

visual information or other ranging data for more robust and

accurate state estimation [21], [22].

B. SLAM benchmarks

There are various multi-sensor datasets available online. We

had a systematic comparison of the popular datasets in the

Table III of our former work [9]. Among these datasets, not

all of them have an analytical benchmark of 3D Lidar SLAM

based on multi-modality Lidars. KITTI benchmark [23] is the

most signiﬁcant one with capabilities of evaluating several

tasks including odometry, SLAM, objects detection, tracking

ans so alike.

III. DATA COLLECTION

Our data collection platform is shown in Fig. 1b, and

details of sensors are listed in Table I. The platform has been

mounted on a mobile wheeled vehicle to adapt to varying

environments. In most scenarios, the platform is manually

pushed or teleoperated, except for the forest environment

where the platform is handheld.

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ABenchmarkforMulti-ModalLidarSLAMwithGroundTruthinGNSS-DeniedEnvironmentsHaSiery§,LiQingqingy§,YuXianjiay,JorgePenaQueraltay,ZhuoZou,TomiWesterlundyyTurkuIntelligentEmbeddedandRoboticSystems(TIERS)Lab,UniversityofTurku,Finland.SchoolofInformationScienceandTechnology,FudanUniverstiy,ChinaEmails:y...

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A Benchmark for Multi-Modal Lidar SLAM with Ground Truth in GNSS-Denied Environments Ha Siery Li Qingqingy Yu Xianjiay Jorge Pe na Queraltay Zhuo Zou Tomi Westerlundy

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