452 research outputs found
MOZARD: Multi-Modal Localization for Autonomous Vehicles in Urban Outdoor Environments
Visually poor scenarios are one of the main sources of failure in visual
localization systems in outdoor environments. To address this challenge, we
present MOZARD, a multi-modal localization system for urban outdoor
environments using vision and LiDAR. By extending our preexisting key-point
based visual multi-session local localization approach with the use of semantic
data, an improved localization recall can be achieved across vastly different
appearance conditions. In particular we focus on the use of curbstone
information because of their broad distribution and reliability within urban
environments. We present thorough experimental evaluations on several driving
kilometers in challenging urban outdoor environments, analyze the recall and
accuracy of our localization system and demonstrate in a case study possible
failure cases of each subsystem. We demonstrate that MOZARD is able to bridge
scenarios where our previous work VIZARD fails, hence yielding an increased
recall performance, while a similar localization accuracy of 0.2m is achieve
Long-Term Urban Vehicle Localization Using Pole Landmarks Extracted from 3-D Lidar Scans
Due to their ubiquity and long-term stability, pole-like objects are well
suited to serve as landmarks for vehicle localization in urban environments. In
this work, we present a complete mapping and long-term localization system
based on pole landmarks extracted from 3-D lidar data. Our approach features a
novel pole detector, a mapping module, and an online localization module, each
of which are described in detail, and for which we provide an open-source
implementation at www.github.com/acschaefer/polex. In extensive experiments, we
demonstrate that our method improves on the state of the art with respect to
long-term reliability and accuracy: First, we prove reliability by tasking the
system with localizing a mobile robot over the course of 15~months in an urban
area based on an initial map, confronting it with constantly varying routes,
differing weather conditions, seasonal changes, and construction sites. Second,
we show that the proposed approach clearly outperforms a recently published
method in terms of accuracy.Comment: 9 page
Magnopark, Smart Parking Detection Based on Cellphone Magnetic Sensor
We introduce a solution that uses the availability of heavy crowds and their smart devices, to gain more result as to where potential parking is possible. By leveraging the raw magnetometer, gyroscope, and accelerometer data, we are able to detect parking spots through the natural movement exerted by the walking pedestrians on the sidewalks beside the streets. Dating back as far as 2013, a very large portion of pedestrians composing the crowds on the sidewalk, possessed at least one smart device in their hand or pocket14]. It is this statistic that fuels our application, in which we depend on crowds or even a steady rate of pedestrians, telling others around them where unoccupied parking sport are, without making a single bit of noise. In other words, we use the walking pedestrians’ cellphone sensors to classify the sidewalk parking spots as occupied and vacant. The more pedestrians walking on the sidewalk, the more accurate our application works. As the years and technological advances both increase, we predict that the number of smart devices will only increase, allowing our solution to become much more precise and useful.
The biggest contribution of our study can be summarized as follows:
• Implementation of Magnopark; a high accuracy parking spot localization system using internal smart phone sensors
• Evaluation and test of Magnopark in different situations and places
• Test of Magnopark for different users with different walking habits and speed
• Development of an algorithm to detect the users’ stride, speed, and direction change
• Building a classification model based on the features extracted from the cellphone sensors
• Pushing the classified data to the cloud for the drivers’ us
ConvBKI: Real-Time Probabilistic Semantic Mapping Network with Quantifiable Uncertainty
In this paper, we develop a modular neural network for real-time semantic
mapping in uncertain environments, which explicitly updates per-voxel
probabilistic distributions within a neural network layer. Our approach
combines the reliability of classical probabilistic algorithms with the
performance and efficiency of modern neural networks. Although robotic
perception is often divided between modern differentiable methods and classical
explicit methods, a union of both is necessary for real-time and trustworthy
performance. We introduce a novel Convolutional Bayesian Kernel Inference
(ConvBKI) layer which incorporates semantic segmentation predictions online
into a 3D map through a depthwise convolution layer by leveraging conjugate
priors. We compare ConvBKI against state-of-the-art deep learning approaches
and probabilistic algorithms for mapping to evaluate reliability and
performance. We also create a Robot Operating System (ROS) package of ConvBKI
and test it on real-world perceptually challenging off-road driving data.Comment: arXiv admin note: text overlap with arXiv:2209.1066
Pre-Trained Driving in Localized Surroundings with Semantic Radar Information and Machine Learning
Entlang der Signalverarbeitungskette von Radar Detektionen bis zur Fahrzeugansteuerung, diskutiert diese Arbeit eine semantischen Radar Segmentierung, einen darauf aufbauenden Radar SLAM, sowie eine im Verbund realisierte autonome Parkfunktion. Die Radarsegmentierung der (statischen) Umgebung wird durch ein Radar-spezifisches neuronales Netzwerk RadarNet erreicht. Diese Segmentierung ermöglicht die Entwicklung des semantischen Radar Graph-SLAM SERALOC. Auf der Grundlage der semantischen Radar SLAM Karte wird eine beispielhafte autonome Parkfunktionalität in einem realen Versuchsträger umgesetzt.
Entlang eines aufgezeichneten Referenzfades parkt die Funktion ausschließlich auf Basis der Radar Wahrnehmung mit bisher unerreichter Positioniergenauigkeit.
Im ersten Schritt wird ein Datensatz von 8.2 · 10^6 punktweise semantisch gelabelten Radarpunktwolken über eine Strecke von 2507.35m generiert. Es sind keine vergleichbaren Datensätze dieser Annotationsebene und Radarspezifikation öffentlich verfügbar. Das überwachte
Training der semantischen Segmentierung RadarNet erreicht 28.97% mIoU auf sechs Klassen.
Außerdem wird ein automatisiertes Radar-Labeling-Framework SeRaLF vorgestellt, welches das Radarlabeling multimodal mittels Referenzkameras und LiDAR unterstützt.
Für die kohärente Kartierung wird ein Radarsignal-Vorfilter auf der Grundlage einer Aktivierungskarte entworfen, welcher Rauschen und andere dynamische Mehrwegreflektionen unterdrückt. Ein speziell für Radar angepasstes Graph-SLAM-Frontend mit Radar-Odometrie
Kanten zwischen Teil-Karten und semantisch separater NDT Registrierung setzt die vorgefilterten semantischen Radarscans zu einer konsistenten metrischen Karte zusammen. Die Kartierungsgenauigkeit und die Datenassoziation werden somit erhöht und der erste semantische Radar Graph-SLAM für beliebige statische Umgebungen realisiert.
Integriert in ein reales Testfahrzeug, wird das Zusammenspiel der live RadarNet Segmentierung und des semantischen Radar Graph-SLAM anhand einer rein Radar-basierten autonomen Parkfunktionalität evaluiert. Im Durchschnitt über 42 autonome Parkmanöver
(∅3.73 km/h) bei durchschnittlicher Manöverlänge von ∅172.75m wird ein Median absoluter Posenfehler von 0.235m und End-Posenfehler von 0.2443m erreicht, der vergleichbare
Radar-Lokalisierungsergebnisse um ≈ 50% übertrifft. Die Kartengenauigkeit von veränderlichen, neukartierten Orten über eine Kartierungsdistanz von ∅165m ergibt eine ≈ 56%-ige Kartenkonsistenz bei einer Abweichung von ∅0.163m. Für das autonome Parken wurde ein gegebener Trajektorienplaner und Regleransatz verwendet
Rapid Localization and Mapping Method Based on Adaptive Particle Filters.
With the development of autonomous vehicles, localization and mapping technologies have become crucial to equip the vehicle with the appropriate knowledge for its operation. In this paper, we extend our previous work by prepossessing a localization and mapping architecture for autonomous vehicles that do not rely on GPS, particularly in environments such as tunnels, under bridges, urban canyons, and dense tree canopies. The proposed approach is of two parts. Firstly, a K-means algorithm is employed to extract features from LiDAR scenes to create a local map of each scan. Then, we concatenate the local maps to create a global map of the environment and facilitate data association between frames. Secondly, the main localization task is performed by an adaptive particle filter that works in four steps: (a) generation of particles around an initial state (provided by the GPS); (b) updating the particle positions by providing the motion (translation and rotation) of the vehicle using an inertial measurement device; (c) selection of the best candidate particles by observing at each timestamp the match rate (also called particle weight) of the local map (with the real-time distances to the objects) and the distances of the particles to the corresponding chunks of the global map; (d) averaging the selected particles to derive the estimated position, and, finally, using a resampling method on the particles to ensure the reliability of the position estimation. The performance of the newly proposed technique is investigated on different sequences of the Kitti and Pandaset raw data with different environmental setups, weather conditions, and seasonal changes. The obtained results validate the performance of the proposed approach in terms of speed and representativeness of the feature extraction for real-time localization in comparison with other state-of-the-art methods
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