Reviewer 2 of ICRA 2011 submission 870

Comments to the author
======================

This paper is about an approach to visual localisation for
an outdoor
vehicle using visual bag of words techniques from image
retrieval.
This is the same kind of machinery as can be used for
topological SLAM
in systems such as FABMAP, although SLAM is not explicitly
attempted
here --- the set-up is assumed to be that the vehicle would
be led
through the environment non-autonomously in a learning
phase, and
would then learn the ability to relocalise itself anywhere
in the
environment later. Relocalisation is tested both in global
mode where
no prior is available on the vehicle location, and in local
model
where localisation must be performed frame to frame along a
sequence.

So the work here is a small variation on previous work on
BoW (bag of
words) localisation which has been used previously various
times in
place recognition and loop closure detection for SLAM
(which is the
same problem as global localisation) --- for instance very
successfully in FABMAP or in the work by Angeli, Filliat et
al., IEEE
Transactions on Robotics 2008, uncited here). The authors
here say
that their approach is particularly tuned to the challenges
of
autonomous navigation on Indian roads, where there are the
two
problems of a lack of unique landmarks and a large number
of
independently moving vehicles and objects. I accept that
Indian roads
are no doubt different from the European conditions where
FABMAP was
successfully tested over very long experiments, but not
that there are
necessarily more unique landmarks in the European scenes
--- as a look
at the results presented showing miles of similar country
roads
prove. The point about dealing with clutter is perhaps a
stronger on,
though western roads are also often very busy!

So the novelties in this paper are firstly an explicit
scheme to throw
out features lying on moving objects. I don't quite
understand why
this couldn't be done with a standard RANSAC approach, and
instead
observations of the scale of features are used which do not
strike me
as likely to be very reliable. Then there is a method to
assign
weights to visual words to push down the effect of high
ambiguous
features in the database, and while this may be done in a
slightly
different way this idea is common in BoW localisation
methods and I
think is done in a more sophisticated way in FABMAP.

Fairly good results are given with error rates which seem
low, but we
are not told about the recall performance --- it is now
common in
recognition papers like this that we should be given a full
recall/precision curve as results so this seems incomplete.

Overall, this is decent work but I see it as currently too
incremental
when compared to the large number of other published papers
on very
similar methods.
