We propose a novel real-time LiDAR intensity image-based simultaneous localization and mapping method, which addresses the geometry degeneracy problem in un-structured environments. Traditional LiDAR-based front-end o...
We propose a novel real-time LiDAR intensity image-based simultaneous localization and mapping method, which addresses the geometry degeneracy problem in un-structured environments. Traditional LiDAR-based front-end odometry mostly relies on geometric features such as points, lines and planes. A lack of these features in the environment can lead to the failure of the entire odometry system. To avoid this problem, we extract feature points from the LiDAR-generated point cloud that match features identified in LiDAR intensity images. We then use the extracted feature points to perform scan registration and estimate the robot ego-movement. For the back-end, we jointly optimize the distance between the corresponding feature points, and the point to plane distance for planes identified in the map. In addition, we use the features extracted from intensity images to detect loop closure candidates from previous scans and perform pose graph optimization. Our experiments show that our method can run in real time with high accuracy and works well with illumination changes, low-texture, and unstructured environments.
作者：
Andreas MadsenSiva ReddySarath ChandarMila
Montreal Canada and Computer Engineering and Software Engineering Department Polytechnique Montreal Montreal Canada Mila
Montreal Canada and Computer Science and Linguistics McGill University Montreal Canada and Facebook CIFAR AI Chair Mila
Montreal Canada and Computer Engineering and Software Engineering Department Polytechnique Montreal Montreal Canada and Canada CIFAR AI Chair
A common approach to explaining NLP models is to use importance measures that express which tokens are important for a prediction. Unfortunately, such explanations are often wrong despite being persuasive. Therefore, ...
A common approach to explaining NLP models is to use importance measures that express which tokens are important for a prediction. Unfortunately, such explanations are often wrong despite being persuasive. Therefore, it is essential to measure their faithfulness. One such metric is if tokens are truly important, then masking them should result in worse model performance. However, token masking introduces out-of-distribution issues, and existing solutions that address this are computationally expensive and employ proxy models. Furthermore, other metrics are very limited in scope. This work proposes an inherently faithfulness measurable model that addresses these challenges. This is achieved using a novel fine-tuning method that incorporates masking, such that masking tokens become indistribution by design. This differs from existing approaches, which are completely model-agnostic but are inapplicable in practice. We demonstrate the generality of our approach by applying it to 16 different datasets and validate it using statistical in-distribution tests. The faithfulness is then measured with 9 different importance measures. Because masking is in-distribution, importance measures that themselves use masking become consistently more faithful. Additionally, because the model makes faithfulness cheap to measure, we can optimize explanations towards maximal faithfulness; thus, our model becomes indirectly inherently explainable.
Integrating Optical Transport Networks (OTNs) into multilayer Elastic Optical Networks (EONs) enhances data transmission efficiency but introduces significant challenges in routing and spectrum allocation, particularl...
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The manufacturing sector was recently affected by workforce shortages, a problem that automation and robotics can heavily minimize. Simultaneously, reinforcement learning (RL) offers a promising solution where robots ...
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A person’s privacy has become a growing concern,given the nature of an expansive reliance on real-time video activities with video capture,stream,and *** paper presents an innovative system design based on a privacy-...
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A person’s privacy has become a growing concern,given the nature of an expansive reliance on real-time video activities with video capture,stream,and *** paper presents an innovative system design based on a privacy-preserving *** proposed system design is implemented by employing an enhanced capability that overcomes today’s single parameterbased access control protection mechanism for digital privacy *** enhanced capability combines multiple access control parameters:facial expression,resource,environment,location,and *** proposed system design demonstrated that a person’s facial expressions combined with a set of access control rules can achieve a person’s privacy-preserving *** findings resulted in different facial expressions successfully triggering a person’s face to be blurred and a person’s privacy when using a real-time video conferencing service captured from a webcam or virtual webcam.A comparison analysis of capabilities between existing designs and the proposed system design shows enhancement of the capabilities of the proposed system.A series of experiments exercising the enhanced,real-time multi-parameterbased system was shown as a viable path forward for preserving a person’s privacy while using a webcam or virtual webcam to capture,stream,and store videos.
作者：
Gosselin, FrancisZouaq, AmalLAMA-WeST Lab.
Departement of Computer Engineering and Software Engineering Polytechnique Montreal 2500 Chem. de Polytechnique MontréalQCH3T 1J4 Canada
This paper presents the results of SORBETMatcher in the OAEI 2023 competition. SORBETMatcher is a schema matching system for both equivalence matching and subsumption matching. SORBETMatcher is largely based on SORBET...
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In this paper, a reinforcement learning technique is employed to maximize the performance of a cognitive radio network (CRN). In the presence of primary users (PUs), it is presumed that two secondary users (SUs) acces...
In this paper, a reinforcement learning technique is employed to maximize the performance of a cognitive radio network (CRN). In the presence of primary users (PUs), it is presumed that two secondary users (SUs) access the licensed band within underlay mode. In addition, the SU transmitter is assumed to be an energy-constrained device that requires harvesting energy in order to transmit signals to their intended destination. Therefore, we propose that there are two main sources of energy; the interference of PUs’ transmissions and ambient radio frequency (RF) sources. The SU will select whether to gather energy from PUs or only from ambient sources based on a predetermined threshold. The process of energy harvesting from the PUs’ messages is accomplished via the time switching approach. In addition, based on a deep Q-network (DQN) approach, the SU transmitter determines whether to collect energy or transmit messages during each time slot as well as selects the suitable transmission power in order to maximize its average data rate. Our approach outperforms a baseline strategy and converges, as shown by our findings.
Broadcasting is an information dissemination primitive where a message is passed from one node (called originator) to all other nodes in the network. In the scope of this paper, we will mainly focus on determining the...
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The emerging behaviors of swarms have fascinated scientists and gathered significant interest in the field of robotics. Traditionally, swarms are viewed as egalitarian, with robots sharing identical roles and capabili...
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Future mobile and terrestrial communication systems B5G/6G are strongly expected to heterogeneously realize typically diversified performances, i.e. high-data-rate, high-mobility, low-latency, high-capacity, massive-c...
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