实时激光SLAM中的Google Cartographer：5厘米分辨率实时建图与环路闭合

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Google Cartographer是一种由Google开发的实时激光雷达SLAM（Simultaneous Localization and Mapping，同时定位与建图）系统，专为便携式激光雷达范围测量设备设计，如LiDAR（Light Detection and Ranging）。SLAM技术在建筑测绘、环境感知、机器人导航等领域有着广泛的应用，它能够实时生成和更新地图，帮助操作人员评估数据质量和覆盖范围。在实际应用中，尤其是在背包式测绘平台中，由于需要在有限的计算资源下运行，实时性和精度是关键考量因素。Google Cartographer采用了一种分支和剪枝方法来计算扫描数据与子地图之间的匹配，作为实现即时循环闭合（Loop Closure）的约束条件。循环闭合是指在地图重建过程中，系统能够识别并确认先前已经探索过的区域，这对于保持地图的准确性至关重要。相比于传统的SLAM算法，Google Cartographer在质量方面表现出竞争力。论文作者Wolfgang Hess、Damon Kohler、Holger Rapp和Daniel Andor展示了他们的背包式平台如何在5厘米分辨率下实现实时建图和循环闭合，这对于高效地获取室内或户外的高精度地形图非常有价值。这种方法避免了手动测量的繁琐，提高了工作效率，并且对于建筑管理、空间规划以及机器人路径规划等任务具有显著的优势。为了实现这一目标，论文详细介绍了以下步骤和技术： 1. **实时数据处理**：利用高效的算法处理来自LiDAR的数据流，确保实时性和响应性。 2. **局部地图构建**：通过连续的激光扫描数据生成实时的局部地图，每个子地图代表当前环境的一个片段。 3. **匹配与循环闭合检测**：通过分支和剪枝策略搜索最佳的匹配，将新扫描与之前构建的子地图进行对比，判断是否为已知区域。 4. **优化地图结构**：当循环闭合被确认后，更新地图结构，合并新信息，提高整体地图的精确度和完整性。 5. **性能评估与比较**：实验结果表明，Google Cartographer在实时性、精度和稳定性上与业界主流方法相当，甚至在某些情况下有所超越。 Google Cartographer是一个强大的工具，它将激光雷达数据融合到SLAM框架中，实现了高效的实时建图和循环闭合，适用于各种对地图精度和实时反馈有严格要求的场景。随着技术的发展，Google Cartographer有望继续推动LiDAR SLAM技术的进步，为未来的自动化测绘和机器人导航提供关键支撑。

Real-Time Loop Closure in 2D LIDAR SLAM

Wolfgang Hess

, Damon Kohler

, Holger Rapp

, Daniel Andor

Abstract— Portable laser range-ﬁnders, further referred to as

LIDAR, and simultaneous localization and mapping (SLAM)

are an efﬁcient method of acquiring as-built ﬂoor plans.

Generating and visualizing ﬂoor plans in real-time helps the

operator assess the quality and coverage of capture data. Build-

ing a portable capture platform necessitates operating under

limited computational resources. We present the approach used

in our backpack mapping platform which achieves real-time

mapping and loop closure at a 5 cm resolution. To achieve real-

time loop closure, we use a branch-and-bound approach for

computing scan-to-submap matches as constraints. We provide

experimental results and comparisons to other well known

approaches which show that, in terms of quality, our approach

is competitive with established techniques.

I. INTRODUCTION

As-built ﬂoor plans are useful for a variety of applications.

Manual surveys to collect this data for building management

tasks typically combine computed-aided design (CAD) with

laser tape measures. These methods are slow and, by em-

ploying human preconceptions of buildings as collections

of straight lines, do not always accurately describe the true

nature of the space. Using SLAM, it is possible to swiftly

and accurately survey buildings of sizes and complexities that

would take orders of magnitude longer to survey manually.

Applying SLAM in this ﬁeld is not a new idea and is

not the focus of this paper. Instead, the contribution of this

paper is a novel method for reducing the computational

requirements of computing loop closure constraints from

laser range data. This technique has enabled us to map

very large ﬂoors, tens-of-thousands of square meters, while

providing the operator fully optimized results in real-time.

II. RELATED WORK

Scan-to-scan matching is frequently used to compute

relative pose changes in laser-based SLAM approaches, for

example [1]–[4]. On its own, however, scan-to-scan matching

quickly accumulates error.

Scan-to-map matching helps limit this accumulation of

error. One such approach, which uses Gauss-Newton to ﬁnd

local optima on a linearly interpolated map, is [5]. In the

presence of good initial estimates for the pose, provided in

this case by using a sufﬁciently high data rate LIDAR, locally

optimized scan-to-map matching is efﬁcient and robust.

On unstable platforms, the laser fan is projected onto the

horizontal plane using an inertial measurement unit (IMU)

to estimate the orientation of gravity.

Pixel-accurate scan matching approaches, such as [1],

further reduce local error accumulation. Although compu-

tationally more expensive, this approach is also useful for

All authors are at Google.

loop closure detection. Some methods focus on improving

on the computational cost by matching on extracted features

from the laser scans [4]. Other approaches for loop closure

detection include histogram-based matching [6], feature de-

tection in scan data, and using machine learning [7].

Two common approaches for addressing the remaining

local error accumulation are particle ﬁlter and graph-based

SLAM [2], [8].

Particle ﬁlters must maintain a representation of the full

system state in each particle. For grid-based SLAM, this

quickly becomes resource intensive as maps become large;

e.g. one of our test cases is 22,000 m

collected over a 3 km

trajectory. Smaller dimensional feature representations, such

as [9], which do not require a grid map for each particle, may

be used to reduce resource requirements. When an up-to-

date grid map is required, [10] suggests computing submaps,

which are updated only when necessary, such that the ﬁnal

map is the rasterization of all submaps.

Graph-based approaches work over a collection of nodes

representing poses and features. Edges in the graph are con-

straints generated from observations. Various optimization

methods may be used to minimize the error introduced by

all constraints, e.g. [11], [12]. Such a system for outdoor

SLAM that uses a graph-based approach, local scan-to-scan

matching, and matching of overlapping local maps based on

histograms of submap features is described in [13].

III. SYSTEM OVERVIEW

Google’s Cartographer provides a real-time solution for

indoor mapping in the form of a sensor equipped backpack

that generates 2D grid maps with a r = 5 cm resolution. The

operator of the system can see the map being created while

walking through a building. Laser scans are inserted into a

submap at the best estimated position, which is assumed to be

sufﬁciently accurate for short periods of time. Scan matching

happens against a recent submap, so it only depends on

recent scans, and the error of pose estimates in the world

frame accumulates.

To achieve good performance with modest hardware re-

quirements, our SLAM approach does not employ a particle

ﬁlter. To cope with the accumulation of error, we regularly

run a pose optimization. When a submap is ﬁnished, that is

no new scans will be inserted into it anymore, it takes part

in scan matching for loop closure. All ﬁnished submaps and

scans are automatically considered for loop closure. If they

are close enough based on current pose estimates, a scan

matcher tries to ﬁnd the scan in the submap. If a sufﬁciently

good match is found in a search window around the currently

estimated pose, it is added as a loop closing constraint to the

2016 IEEE International Conference on Robotics and Automation (ICRA)

Stockholm, Sweden, May 16-21, 2016

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