96 research outputs found
LocNet: Global localization in 3D point clouds for mobile vehicles
Global localization in 3D point clouds is a challenging problem of estimating
the pose of vehicles without any prior knowledge. In this paper, a solution to
this problem is presented by achieving place recognition and metric pose
estimation in the global prior map. Specifically, we present a semi-handcrafted
representation learning method for LiDAR point clouds using siamese LocNets,
which states the place recognition problem to a similarity modeling problem.
With the final learned representations by LocNet, a global localization
framework with range-only observations is proposed. To demonstrate the
performance and effectiveness of our global localization system, KITTI dataset
is employed for comparison with other algorithms, and also on our long-time
multi-session datasets for evaluation. The result shows that our system can
achieve high accuracy.Comment: 6 pages, IV 2018 accepte
Free-Space Features: Global Localization in 2D Laser SLAM Using Distance Function Maps
In many applications, maintaining a consistent map of the environment is key
to enabling robotic platforms to perform higher-level decision making.
Detection of already visited locations is one of the primary ways in which map
consistency is maintained, especially in situations where external positioning
systems are unavailable or unreliable. Mapping in 2D is an important field in
robotics, largely due to the fact that man-made environments such as warehouses
and homes, where robots are expected to play an increasing role, can often be
approximated as planar. Place recognition in this context remains challenging:
2D lidar scans contain scant information with which to characterize, and
therefore recognize, a location. This paper introduces a novel approach aimed
at addressing this problem. At its core, the system relies on the use of the
distance function for representation of geometry. This representation allows
extraction of features which describe the geometry of both surfaces and
free-space in the environment. We propose a feature for this purpose. Through
evaluations on public datasets, we demonstrate the utility of free-space in the
description of places, and show an increase in localization performance over a
state-of-the-art descriptor extracted from surface geometry
A Survey on Global LiDAR Localization
Knowledge about the own pose is key for all mobile robot applications. Thus
pose estimation is part of the core functionalities of mobile robots. In the
last two decades, LiDAR scanners have become a standard sensor for robot
localization and mapping. This article surveys recent progress and advances in
LiDAR-based global localization. We start with the problem formulation and
explore the application scope. We then present the methodology review covering
various global localization topics, such as maps, descriptor extraction, and
consistency checks. The contents are organized under three themes. The first is
the combination of global place retrieval and local pose estimation. Then the
second theme is upgrading single-shot measurement to sequential ones for
sequential global localization. The third theme is extending single-robot
global localization to cross-robot localization on multi-robot systems. We end
this survey with a discussion of open challenges and promising directions on
global lidar localization
- …