  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.
