1,873 research outputs found
Radar-Only Off-Road Local Navigation
Off-road robotics have traditionally utilized lidar for local navigation due
to its accuracy and high resolution. However, the limitations of lidar, such as
reduced performance in harsh environmental conditions and limited range, have
prompted the exploration of alternative sensing technologies. This paper
investigates the potential of radar for off-road local navigation, as it offers
the advantages of a longer range and the ability to penetrate dust and light
vegetation. We adapt existing lidar-based methods for radar and evaluate the
performance in comparison to lidar under various off-road conditions. We show
that radar can provide a significant range advantage over lidar while
maintaining accuracy for both ground plane estimation and obstacle detection.
And finally, we demonstrate successful autonomous navigation at a speed of 2.5
m/s over a path length of 350 m using only radar for ground plane estimation
and obstacle detection.Comment: 7 pages, 17 figures, ITSC 202
DESIGNING AND EVALUATING A PORTABLE LIDAR-BASED SLAM SYSTEM
Mobile Mapping Technology (MMT) has evolved rapidly over the past few decades, especially in using low-cost sensors. This progress is primarily attributed to the appearance of innovative simultaneous localization and mapping (SLAM) algorithms. This article focuses on evaluating the efficiency of a new LiDAR-based portable SLAM system for mapping in dynamic real-world environments. The work proposed a technical solution based on a Livox Avia LiDAR sensor enhanced by gimbal stabilization. The system, named Portable Livox-based Mapping system (PoLiMap), is compared to other similar solutions by acquiring data from various environments, including urban sceneries, underground tunnels and forested areas, and processing them using a modified FAST-LIO-SLAM algorithm. The research presented in the article contributes to the understanding of the capabilities of PoLiMap systems under various conditions and offers significant insight into its potential applications. Accuracy evaluation results prove that the proposed MMT system can successfully tackle various demanding environments and challenge the results of other more costly state-of-the-art portable mobile laser scanning methods
UWB Radar SLAM: an Anchorless Approach in Vision Denied Indoor Environments
LiDAR and cameras are frequently used as sensors for simultaneous
localization and mapping (SLAM). However, these sensors are prone to failure
under low visibility (e.g. smoke) or places with reflective surfaces (e.g.
mirrors). On the other hand, electromagnetic waves exhibit better penetration
properties when the wavelength increases, thus are not affected by low
visibility. Hence, this paper presents ultra-wideband (UWB) radar as an
alternative to the existing sensors. UWB is generally known to be used in
anchor-tag SLAM systems. One or more anchors are installed in the environment
and the tags are attached to the robots. Although this method performs well
under low visibility, modifying the existing infrastructure is not always
feasible. UWB has also been used in peer-to-peer ranging collaborative SLAM
systems. However, this requires more than a single robot and does not include
mapping in the mentioned environment like smoke. Therefore, the presented
approach in this paper solely depends on the UWB transceivers mounted on-board.
In addition, an extended Kalman filter (EKF) SLAM is used to solve the SLAM
problem at the back-end. Experiments were conducted and demonstrated that the
proposed UWB-based radar SLAM is able to map natural point landmarks inside an
indoor environment while improving robot localization
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Remote sensing of tidal networks and their relation to vegetation
The study of the morphology of tidal networks and their relation to salt marsh vegetation is currently an active area of research, and a number of theories have been developed which require validation using extensive observations. Conventional methods of measuring networks and associated vegetation can be cumbersome and subjective. Recent advances in remote sensing techniques mean that these can now often reduce measurement effort whilst at the same time increasing measurement scale. The status of remote sensing of tidal networks and their relation to vegetation is reviewed. The measurement of network planforms and their associated variables is possible to sufficient resolution using digital aerial photography and airborne scanning laser altimetry (LiDAR), with LiDAR also being able to measure channel depths. A multi-level knowledge-based technique is described to extract networks from LiDAR in a semi-automated fashion. This allows objective and detailed geomorphological information on networks to be obtained over large areas of the inter-tidal zone. It is illustrated using LIDAR data of the River Ems, Germany, the Venice lagoon, and Carnforth Marsh, Morecambe Bay, UK. Examples of geomorphological variables of networks extracted from LiDAR data are given. Associated marsh vegetation can be classified into its component species using airborne hyperspectral and satellite multispectral data. Other potential applications of remote sensing for network studies include determining spatial relationships between networks and vegetation, measuring marsh platform vegetation roughness, in-channel velocities and sediment processes, studying salt pans, and for marsh restoration schemes
Non-contact Multimodal Indoor Human Monitoring Systems: A Survey
Indoor human monitoring systems leverage a wide range of sensors, including
cameras, radio devices, and inertial measurement units, to collect extensive
data from users and the environment. These sensors contribute diverse data
modalities, such as video feeds from cameras, received signal strength
indicators and channel state information from WiFi devices, and three-axis
acceleration data from inertial measurement units. In this context, we present
a comprehensive survey of multimodal approaches for indoor human monitoring
systems, with a specific focus on their relevance in elderly care. Our survey
primarily highlights non-contact technologies, particularly cameras and radio
devices, as key components in the development of indoor human monitoring
systems. Throughout this article, we explore well-established techniques for
extracting features from multimodal data sources. Our exploration extends to
methodologies for fusing these features and harnessing multiple modalities to
improve the accuracy and robustness of machine learning models. Furthermore, we
conduct comparative analysis across different data modalities in diverse human
monitoring tasks and undertake a comprehensive examination of existing
multimodal datasets. This extensive survey not only highlights the significance
of indoor human monitoring systems but also affirms their versatile
applications. In particular, we emphasize their critical role in enhancing the
quality of elderly care, offering valuable insights into the development of
non-contact monitoring solutions applicable to the needs of aging populations.Comment: 19 pages, 5 figure
Lidar-level localization with radar? The CFEAR approach to accurate, fast and robust large-scale radar odometry in diverse environments
This paper presents an accurate, highly efficient, and learning-free method
for large-scale odometry estimation using spinning radar, empirically found to
generalize well across very diverse environments -- outdoors, from urban to
woodland, and indoors in warehouses and mines - without changing parameters.
Our method integrates motion compensation within a sweep with one-to-many scan
registration that minimizes distances between nearby oriented surface points
and mitigates outliers with a robust loss function. Extending our previous
approach CFEAR, we present an in-depth investigation on a wider range of data
sets, quantifying the importance of filtering, resolution, registration cost
and loss functions, keyframe history, and motion compensation. We present a new
solving strategy and configuration that overcomes previous issues with sparsity
and bias, and improves our state-of-the-art by 38%, thus, surprisingly,
outperforming radar SLAM and approaching lidar SLAM. The most accurate
configuration achieves 1.09% error at 5Hz on the Oxford benchmark, and the
fastest achieves 1.79% error at 160Hz.Comment: Accepted for publication in Transactions on Robotics. Edited
2022-11-07: Updated affiliation and citatio
Kidnapped Radar: Topological Radar Localisation using Rotationally-Invariant Metric Learning
This paper presents a system for robust, large-scale topological localisation
using Frequency-Modulated Continuous-Wave (FMCW) scanning radar. We learn a
metric space for embedding polar radar scans using CNN and NetVLAD
architectures traditionally applied to the visual domain. However, we tailor
the feature extraction for more suitability to the polar nature of radar scan
formation using cylindrical convolutions, anti-aliasing blurring, and
azimuth-wise max-pooling; all in order to bolster the rotational invariance.
The enforced metric space is then used to encode a reference trajectory,
serving as a map, which is queried for nearest neighbours (NNs) for recognition
of places at run-time. We demonstrate the performance of our topological
localisation system over the course of many repeat forays using the largest
radar-focused mobile autonomy dataset released to date, totalling 280 km of
urban driving, a small portion of which we also use to learn the weights of the
modified architecture. As this work represents a novel application for FMCW
radar, we analyse the utility of the proposed method via a comprehensive set of
metrics which provide insight into the efficacy when used in a realistic
system, showing improved performance over the root architecture even in the
face of random rotational perturbation.Comment: submitted to the 2020 International Conference on Robotics and
Automation (ICRA
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