Augmenting Sparse Laser Scans with
Virtual Scans to Improve the Performance
of Alignment Algorithms
Department of Computer and Information Science Temple University, Philadelphia, USA
We present a system to increase the performance of feature correspondence based alignment
algorithms for laser scan data. Alignment approaches for robot mapping, like ICP or FFS,
perform successfully only under the condition of sufficient feature overlap between single
scans. This condition is often not met, e.g. in sparsely scanned environments or disaster
areas for search and rescue robot tasks. Assuming mid level world knowledge (in the
presented case the weak presence of noisy, roughly linear or rectangular-like objects) our
system augments the sensor data with hypotheses ('Virtual Scans') about ideal models of
these objects, based on analysis of a current estimated map of the underlying iterative
alignment algorithm. Feedback between the data alignment and the data analysis confirms,
modifies, or discards the Virtual Scan data in each iteration. Experiments with a simulated
scenario and real world data from a rescue robot scenario show the applicability and
advantages of the approach.
Robot mapping based on laser range scans is a major field of research in robotics in the
recent years. The basic task of mapping is to combine spatial data usually gained from laser
range devices, called 'scans', to a single data set, the 'global map'. The global map represents
the environment scanned from different locations, even possibly scanned by different robots
('multi robot mapping'), usually without knowledge of their pose (= position and heading).
One class of approaches to tackle this problem, i.e. to align single scans, is based on feature
correspondences between the single scans to find optimal correspondence configurations.
Techniques like ICP (Iterative Closest Point, e.g. [2, 24] and ) or FFS (Force Field
Simulation based alignment, ) belong to this class. They show impressive results, but are
naturally restricted: first since they are feature correspondence based, they require the
presence of a sufficient amount of common, overlapping features in scans belonging
together. Second, since the feature correspondence function is based on a state describing
the relation of the single scans (e.g. the robots' poses), these algorithms are depending on
sufficiently good state initialization to avoid local minima. In this paper, we suggest a
solution to the first problem: correct alignment in the absence of sufficient feature
correspondences. This problem can e.g. arise in search and rescue environments (these
environments typically show a little number of landmarks only) or when multiple robots
team to build a joint global map. In this situation, single scans, acquired from different
views, do not necessarily reveal the entire structure of the scanned object. The motivation to
our approach is that even if the optimal relation between single scans is not known, it is
possible to infer hypotheses of underlying structures from the non-optimal combination of
single scans based on the assumption of certain real world knowledge. Figure 1 illustrates
Fig. 1. Motivation of Virtual Scan approach (a-f in reading order): a) rectangular object
scanned from two positions (red/blue robots). b) correspondence between single scans
(red/blue) does not reveal the scanned structure c) misalignment due to wrong
correspondences d) analysis of estimated global map detects structure e) structure is added
as Virtual Scan f) correct alignment achieved due to correspondences between real world
scans and Virtual Scans
idea. It shows a situation where the relation between features of single scans can not reveal
the real world structure, and therefore leads to misalignment. Analysis from a global view
estimates the underlying structure. This hypothesis then augments the real world data set,
to achieve a correct result.
The motivational example shows the ideal case; it doesn't assume any error in the global
map estimation (the relative pose between red and blue scan), hence it is trivial to detect the
correct structure. Our system also handles the non ideal situation including pose errors. It
utilizes a feedback structure between hypothesis generation and real data alignment
response. The feedback iteratively adjusts the hypotheses to the real data (and vice versa).
This will be discussed in more detail below. We first want to explain our approach in a more
Feature correspondence algorithms, e.g. in ICP or FFS, can be seen as low level spatial
cognition processes (LLSC), since they operate based on low level geometric information.
The feature analysis of the global map, which is suggested in this paper, can be described as
mid level spatial cognition process (MLSC), since we aim at analysis of features like lines,
rectangles, etc. Augmenting real world data with ideal models of expected data can be seen
as an example of integration of LLSC and MLSC processes to improve the performance of
spatial recognition tasks in robotics. We are using the area of robot perception for mobile
rescue robots, specifically alignment of 2D laser scans, as a showcase to demonstrate the
advantages of these processes.
In robot cognition, MLSC processes infer the presence of mid level features from low level
data based on regional properties of the data. In our case, we detect the presence of simple
mid level objects, i.e. line segments and rectangles. The MLSC processes model world
knowledge, or assumptions about the environment. In our setting for search and rescue
environments, we assume the presence of (collapsed) walls and other man made structures.
If possible wall-like elements or elements somewhat resembling rectangular structures are
detected, our system generates the most likely ideal model as a hypothesis, called 'Virtual
Scan'. Virtual Scans are generated from the ideal, expected model in the same data format as
the raw sensor data, hence Virtual Scans are added to the original scan data
indistinguishably for the low level alignment process; the alignment is then performed on
the augmented data set.
In robot cognition, LLSC processes usually describe feature extraction based on local
properties like spatial proximity, e.g. based on metric inferences on data points, like edges in
images or laser reflection points. In our system laser scans (virtual or real) are aligned to a
global map using mainly features of local proximity using the LLSC core process of 'Force
Field Simulation' (FFS). FFS was recently introduced to robotics . In FFS, each data point
can be assigned a weight, or value of certainty. It also does not make a hard, but soft
decision about the data correspondences as a basis for the alignment. Both features make
FFS a natural choice over its main competitor, ICP [2, 24], for the combination with Virtual
Scans. The weight parameter can be utilized to indicate the strength of hypotheses,
represented by the weight of virtual data.
FFS is an iterative alignment algorithm. The two levels (LLSC: data alignment by FFS,
MLSC: data augmentation) are connected by a feedback structure, which is repeated in each
• The FFS-low-level-instances pre-process the data. They find correspondences based
on low level features. The low level processing builds a current version of the global
map, which assists the mid-level feature detection
• The mid level cognition module analyzes the current global map, detects possible mid
level objects and models ideal hypothetical sources possibly
being present in the real world. These can be seen as suggestions, fed back into the
low level system by Virtual Scans. The low level system in turn adjusts its processing
for re-evaluation by the mid level systems.
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Fig. 12. Alignment of NIST data set (initial alignment see fig. 11). (a-f in reading order) a) iteration
4. b) iteration 8. c) iteration 10. In (a-c) red lines show the data of the Virtual Scan (VS). Please
notice the mutual adjustment of hypotheses and real data. d) final result using Virtual Scans, after
100 iterations. The VS is not shown for clarity of display. Compare to f): final result of alignment
without VS. Encircled areas show examples of improvement between (d) and (f). e) The center
rectangle could only be aligned correctly using VS information. Please compare (e) to
motivational examples fig. 1 and 3.