1,187 research outputs found
Multi-Session, Localization-oriented and Lightweight LiDAR Mapping Using Semantic Lines and Planes
In this paper, we present a centralized framework for multi-session LiDAR
mapping in urban environments, by utilizing lightweight line and plane map
representations instead of widely used point clouds. The proposed framework
achieves consistent mapping in a coarse-to-fine manner. Global place
recognition is achieved by associating lines and planes on the Grassmannian
manifold, followed by an outlier rejection-aided pose graph optimization for
map merging. Then a novel bundle adjustment is also designed to improve the
local consistency of lines and planes. In the experimental section, both public
and self-collected datasets are used to demonstrate efficiency and
effectiveness. Extensive results validate that our LiDAR mapping framework
could merge multi-session maps globally, optimize maps incrementally, and is
applicable for lightweight robot localization.Comment: Accepted by IROS202
A Novel Perception and Semantic Mapping Method for Robot Autonomy in Orchards
In this work, we propose a novel framework for achieving robotic autonomy in
orchards. It consists of two key steps: perception and semantic mapping. In the
perception step, we introduce a 3D detection method that accurately identifies
objects directly on point cloud maps. In the semantic mapping step, we develop
a mapping module that constructs a visibility graph map by incorporating
object-level information and terrain analysis. By combining these two steps,
our framework improves the autonomy of agricultural robots in orchard
environments. The accurate detection of objects and the construction of a
semantic map enable the robot to navigate autonomously, perform tasks such as
fruit harvesting, and acquire actionable information for efficient agricultural
production
Localization in Unstructured Environments: Towards Autonomous Robots in Forests with Delaunay Triangulation
Autonomous harvesting and transportation is a long-term goal of the forest
industry. One of the main challenges is the accurate localization of both
vehicles and trees in a forest. Forests are unstructured environments where it
is difficult to find a group of significant landmarks for current fast
feature-based place recognition algorithms. This paper proposes a novel
approach where local observations are matched to a general tree map using the
Delaunay triangularization as the representation format. Instead of point cloud
based matching methods, we utilize a topology-based method. First, tree trunk
positions are registered at a prior run done by a forest harvester. Second, the
resulting map is Delaunay triangularized. Third, a local submap of the
autonomous robot is registered, triangularized and matched using triangular
similarity maximization to estimate the position of the robot. We test our
method on a dataset accumulated from a forestry site at Lieksa, Finland. A
total length of 2100\,m of harvester path was recorded by an industrial
harvester with a 3D laser scanner and a geolocation unit fixed to the frame.
Our experiments show a 12\,cm s.t.d. in the location accuracy and with
real-time data processing for speeds not exceeding 0.5\,m/s. The accuracy and
speed limit is realistic during forest operations
Milestones in Autonomous Driving and Intelligent Vehicles Part II: Perception and Planning
Growing interest in autonomous driving (AD) and intelligent vehicles (IVs) is
fueled by their promise for enhanced safety, efficiency, and economic benefits.
While previous surveys have captured progress in this field, a comprehensive
and forward-looking summary is needed. Our work fills this gap through three
distinct articles. The first part, a "Survey of Surveys" (SoS), outlines the
history, surveys, ethics, and future directions of AD and IV technologies. The
second part, "Milestones in Autonomous Driving and Intelligent Vehicles Part I:
Control, Computing System Design, Communication, HD Map, Testing, and Human
Behaviors" delves into the development of control, computing system,
communication, HD map, testing, and human behaviors in IVs. This part, the
third part, reviews perception and planning in the context of IVs. Aiming to
provide a comprehensive overview of the latest advancements in AD and IVs, this
work caters to both newcomers and seasoned researchers. By integrating the SoS
and Part I, we offer unique insights and strive to serve as a bridge between
past achievements and future possibilities in this dynamic field.Comment: 17pages, 6figures. IEEE Transactions on Systems, Man, and
Cybernetics: System
Change of Scenery: Unsupervised LiDAR Change Detection for Mobile Robots
This paper presents a fully unsupervised deep change detection approach for
mobile robots with 3D LiDAR. In unstructured environments, it is infeasible to
define a closed set of semantic classes. Instead, semantic segmentation is
reformulated as binary change detection. We develop a neural network,
RangeNetCD, that uses an existing point-cloud map and a live LiDAR scan to
detect scene changes with respect to the map. Using a novel loss function,
existing point-cloud semantic segmentation networks can be trained to perform
change detection without any labels or assumptions about local semantics. We
demonstrate the performance of this approach on data from challenging terrains;
mean intersection over union (mIoU) scores range between 67.4% and 82.2%
depending on the amount of environmental structure. This outperforms the
geometric baseline used in all experiments. The neural network runs faster than
10Hz and is integrated into a robot's autonomy stack to allow safe navigation
around obstacles that intersect the planned path. In addition, a novel method
for the rapid automated acquisition of per-point ground-truth labels is
described. Covering changed parts of the scene with retroreflective materials
and applying a threshold filter to the intensity channel of the LiDAR allows
for quantitative evaluation of the change detector.Comment: 7 pages (6 content, 1 references). 7 figures, submitted to the 2024
IEEE International Conference on Robotics and Automation (ICRA
- …