Probabilistic Data Association for Semantic SLAM
Sean L. Bowman Nikolay Atanasov Kostas Daniilidis George J. Pappas
Abstract— Traditional approaches to simultaneous localiza-
tion and mapping (SLAM) rely on low-level geometric features
such as points, lines, and planes. They are unable to assign
semantic labels to landmarks observed in the environment.
Furthermore, loop closure recognition based on low-level fea-
tures is often viewpoint-dependent and subject to failure in
ambiguous or repetitive environments. On the other hand,
object recognition methods can infer landmark classes and
scales, resulting in a small set of easily recognizable landmarks,
ideal for view-independent unambiguous loop closure. In a
map with several objects of the same class, however, a crucial
data association problem exists. While data association and
recognition are discrete problems usually solved using discrete
inference, classical SLAM is a continuous optimization over
metric information. In this paper, we formulate an optimization
problem over sensor states and semantic landmark positions
that integrates metric information, semantic information, and
data associations, and decompose it into two interconnected
problems: an estimation of discrete data association and land-
mark class probabilities, and a continuous optimization over the
metric states. The estimated landmark and robot poses affect
the association and class distributions, which in turn affect
the robot-landmark pose optimization. The performance of our
algorithm is demonstrated on indoor and outdoor datasets.
I. INTRODUCTION
In robotics, simultaneous localization and mapping (SLAM)
is the problem of mapping an unknown environment while
estimating a robot’s pose within it. Reliable navigation, object
manipulation, autonomous surveillance, and many other tasks
require accurate knowledge of the robot’s pose and the
surrounding environment. Traditional approaches to SLAM
rely on low-level geometric features such as corners [1],
lines [2], and surface patches [3] to reconstruct the metric
3-D structure of a scene but are mostly unable to infer
semantic content. On the other hand, recent methods for
object recognition [4]–[6] can be combined with approximate
3D reconstruction of the environmental layout from single
frames using priors [7], [8]. These are rather qualitative single
3D snapshots rather than the more precise mapping we need
for a robot to navigate. The goal of this paper is to address the
metric and semantic SLAM problems jointly, taking advantage
of object recognition to tightly integrate both metric and
semantic information into the sensor state and map estimation.
In addition to providing a meaningful interpretation of the
scene, semantically-labeled landmarks address two critical
issues of geometric SLAM: data association (matching sensor
observations to map landmarks) and loop closure (recognizing
previously-visited locations).
The authors are with GRASP Lab, University of Pennsylvania,
Philadelphia, PA 19104, USA,
{seanbow, atanasov, kostas,
pappasg}@seas.upenn.edu.
We gratefully acknowledge support by TerraSwarm, one of six centers
of STARnet, a Semiconductor Research Corporation program sponsored by
MARCO and DARPA and the following grants: ARL MAST-CTA W911NF-
08-2-0004, ARL RCTA W911NF-10-2-0016.
Approaches to SLAM were initially most often based
on filtering methods in which only the most recent robot
pose is estimated [9]. This approach is in general very
computationally efficient, however because of the inability
to estimate past poses and relinearize previous measurement
functions, errors can compound [1]. More recently, batch
methods that optimize over entire trajectories have gained
popularity. Successful batch methods typically represent
optimization variables as a set of nodes in a graph (a “pose
graph”). Two robot-pose nodes share an edge if an odometry
measurement is available between them, while a landmark
and a robot-pose node share an edge if the landmark was
observed from the corresponding robot pose. This pose graph
optimization formulation of SLAM traces back to Lu and
Milios [10]. In recent years, the state of the art [11], [12]
consists of iterative optimization methods (e.g., nonlinear
least squares via the Gauss-Newton algorithm) that achieve
excellent performance but depend heavily on linearization
of the sensing and motion models. This becomes a problem
when we consider including discrete observations, such as
detected object classes, in the sensing model.
One of the first systems that used both spatial and semantic
representations was proposed by Galindo et al. [13]. A spatial
hierarchy contained camera images, local metric maps, and the
environment topology, while a semantic hierarchy represented
concepts and relations, which allowed room categories to be
inferred based on object detections. Many other approaches
[14]–[19] extract both metric and semantic information but
typically the two processes are carried out separately and
the results are merged afterwards. The lack of integration
between the metric and the semantic mapping does not allow
the object detection confidence to influence the performance
of the metric optimization. Focusing on the localization
problem only, Atanasov et al. [20] incorporated semantic
observations in the metric optimization via a set-based Bayes
filter. The works that are closest to ours [21]–[24] consider
both localization and mapping and carry out metric and
semantic mapping jointly. SLAM++ [22] focuses on a real-
time implementation of joint 3-D object recognition and RGB-
D SLAM via pose graph optimization. A global optimization
for 3D reconstruction and semantic parsing has been proposed
by [25], which is the closest work in semantic/geometric joint
optmization. The main difference is that 3D space is voxelized
and landmarks and/or semantic labels are assigned to voxels
which are connected in a conditional random field while
our approach allows the estimation of continuous pose of
objects. Bao et al. [21] incorporate camera parameters, object
geometry, and object classes into a structure from motion
problem, resulting in a detailed and accurate but large and
expensive optimization. A recent comprehensive survey of
semantic mapping can be found in [26].
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