960 research outputs found
Online 3D Mapping and Localization System for Agricultural Robots
For an intelligent agricultural robot to reliably operate on a large-scale farm, it is crucial to accurately estimate its pose. In large outdoor environments, 3D LiDAR is a preferred sensor. Urban and agricultural scenarios are characteristically different, where the latter contains many poorly defined objects such as grass and trees with leaves that will generate noisy sensor signals. While state-of-the-art methods of state estimation using LiDAR, such as LiDAR odometry and mapping (LOAM), work well in urban scenarios, they will fail in the agricultural domain. Hence, we propose a mapping and localization system to cope with challenging agricultural scenarios. Our system maintains a high quality global map for subsequent reuses of relocalization or motion planning. This is beneficial as we avoid the unnecessary repetitively mapping process. Our experimental results show that we achieve comparable or better performance in state estimation, localization, and map quality when compared to LOAM
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
Milli-RIO: Ego-Motion Estimation with Low-Cost Millimetre-Wave Radar
Robust indoor ego-motion estimation has attracted significant interest in the
last decades due to the fast-growing demand for location-based services in
indoor environments. Among various solutions, frequency-modulated
continuous-wave (FMCW) radar sensors in millimeter-wave (MMWave) spectrum are
gaining more prominence due to their intrinsic advantages such as penetration
capability and high accuracy. Single-chip low-cost MMWave radar as an emerging
technology provides an alternative and complementary solution for robust
ego-motion estimation, making it feasible in resource-constrained platforms
thanks to low-power consumption and easy system integration. In this paper, we
introduce Milli-RIO, an MMWave radar-based solution making use of a single-chip
low-cost radar and inertial measurement unit sensor to estimate
six-degrees-of-freedom ego-motion of a moving radar. Detailed quantitative and
qualitative evaluations prove that the proposed method achieves precisions on
the order of few centimeters for indoor localization tasks.Comment: Submitted to IEEE Sensors, 9page
Scalable Estimation of Precision Maps in a MapReduce Framework
This paper presents a large-scale strip adjustment method for LiDAR mobile
mapping data, yielding highly precise maps. It uses several concepts to achieve
scalability. First, an efficient graph-based pre-segmentation is used, which
directly operates on LiDAR scan strip data, rather than on point clouds.
Second, observation equations are obtained from a dense matching, which is
formulated in terms of an estimation of a latent map. As a result of this
formulation, the number of observation equations is not quadratic, but rather
linear in the number of scan strips. Third, the dynamic Bayes network, which
results from all observation and condition equations, is partitioned into two
sub-networks. Consequently, the estimation matrices for all position and
orientation corrections are linear instead of quadratic in the number of
unknowns and can be solved very efficiently using an alternating least squares
approach. It is shown how this approach can be mapped to a standard key/value
MapReduce implementation, where each of the processing nodes operates
independently on small chunks of data, leading to essentially linear
scalability. Results are demonstrated for a dataset of one billion measured
LiDAR points and 278,000 unknowns, leading to maps with a precision of a few
millimeters.Comment: ACM SIGSPATIAL'16, October 31-November 03, 2016, Burlingame, CA, US
- …