Global localization and kidnapping are two challenging problems in robot localization. The popular method, Monte Carlo Localization (MCL) addresses the problem by sampling uniformly ] over the state space , which is unfortunately inefficient when the environment is large . To better deal with the the problems, we present a  proposal model, named Deep Multimodal Observation Model ( DMOM). DMOM takes a map and a 2D laser scan as inputs and outputs a conditional multimodal probability distribution of the pose, making the samples more focusing on the regions with higher likelihood. With such samples, the convergence is expected to be much efficient. Considering that learning based Samplable Observation Model may fail to capture the true pose sometimes, we furthermore propose the Adaptive Mixture MCL , which adaptively selects updating mode for each particle to tolerate this situation. Equipped with DMOM, Adaptive Mixture MCL can achieve more accurate estimation, faster convergence and better scalability compared with previous methods in both synthetic and real scenes. Even in real environment with long-term changing, Adaptive Mixture MCL is able to localize the robot using DMON trained only on simulated observations from a SLAM map , or even a blueprint map.
Global localization and kidnapping are two challenging problems in robot localization. The popular method, Monte Carlo Localization (MCL) addresses the problem by iteratively updating a set of particles with a "sampling-weighting" loop. Sampling is decisive to the performance of MCL 1]. However, traditional MCL can only sample from a uniform distribution over the state space . Although variants of MCL propose different sampling models, they fail to provide an accurate distribution or generalize across scenes . To better deal with these problems, we present a distribution proposal model, named Deep Samplable Observation Model ( DSOM). DSOM takes a map and a 2D laser scan as inputs and outputs a conditional multimodal probability distribution of the pose, making the samples more focusing on the regions with higher likelihood. With such samples, the convergence is expected to be more effective and efficient. Considering that the learning-based sampling model may fail to capture the true pose sometimes, we furthermore propose the Adaptive Mixture MCL (AdaM MCL), which deploys a trusty mechanism to adaptively select updating mode for each particle to tolerate this situation. Equipped with DSOM, AdaM MCL can achieve more accurate estimation, faster convergence and better scalability compared to previous methods in both synthetic and real scenes. Even in real environments with long-term changing, AdaM MCL is able to localize the robot using DSOM trained only by simulation observations from a SLAM map or a blueprint map.
R
sampling uniformly
iteratively updating a set of particles with a "sampling-weighting" loop. Sampling is decisive to the performance of MCL
A
1
A
. However, traditional MCL can only sample from a uniform distribution
R
, which is unfortunately inefficient when the environment is large
. Although variants of MCL propose different sampling models, they fail to provide an accurate distribution or generalize across scenes
R
the the
these
A
distribution
R
Multimodal
Samplable
R
DMOM). DMOM
DSOM). DSOM
R
much
more effective and
R
learning based Samplable Observation Model
the learning-based sampling model
R
, which adaptively selects
(AdaM MCL), which deploys a trusty mechanism to adaptively select
R
DMOM, Adaptive Mixture
DSOM, AdaM
R
with
to
R
environment
environments
R
Adaptive Mixture
AdaM
R
DMON trained only on simulated
DSOM trained only by simulation
R
, or even
or