7 research outputs found
A stacked LSTM based approach for reducing semantic pose estimation error
© 1963-2012 IEEE. Achieving high estimation accuracy is significant for semantic simultaneous localization and mapping (SLAM) tasks. Yet, the estimation process is vulnerable to several sources of error, including limitations of the instruments used to perceive the environment, shortcomings of the employed algorithm, environmental conditions, or other unpredictable noise. In this article, a novel stacked long short-term memory (LSTM)-based error reduction approach is developed to enhance the accuracy of semantic SLAM in presence of such error sources. Training and testing data sets were constructed through simulated and real-time experiments. The effectiveness of the proposed approach was demonstrated by its ability to capture and reduce semantic SLAM estimation errors in training and testing data sets. Quantitative performance measurement was carried out using the absolute trajectory error (ATE) metric. The proposed approach was compared with vanilla and bidirectional LSTM networks, shallow and deep neural networks, and support vector machines. The proposed approach outperforms all other structures and was able to significantly improve the accuracy of semantic SLAM. To further verify the applicability of the proposed approach, it was tested on real-time sequences from the TUM RGB-D data set, where it was able to improve the estimated trajectories
Neuromorphic Camera Denoising using Graph Neural Network-driven Transformers
Neuromorphic vision is a bio-inspired technology that has triggered a
paradigm shift in the computer-vision community and is serving as a key-enabler
for a multitude of applications. This technology has offered significant
advantages including reduced power consumption, reduced processing needs, and
communication speed-ups. However, neuromorphic cameras suffer from significant
amounts of measurement noise. This noise deteriorates the performance of
neuromorphic event-based perception and navigation algorithms. In this paper,
we propose a novel noise filtration algorithm to eliminate events which do not
represent real log-intensity variations in the observed scene. We employ a
Graph Neural Network (GNN)-driven transformer algorithm, called
GNN-Transformer, to classify every active event pixel in the raw stream into
real-log intensity variation or noise. Within the GNN, a message-passing
framework, called EventConv, is carried out to reflect the spatiotemporal
correlation among the events, while preserving their asynchronous nature. We
also introduce the Known-object Ground-Truth Labeling (KoGTL) approach for
generating approximate ground truth labels of event streams under various
illumination conditions. KoGTL is used to generate labeled datasets, from
experiments recorded in chalenging lighting conditions. These datasets are used
to train and extensively test our proposed algorithm. When tested on unseen
datasets, the proposed algorithm outperforms existing methods by 8.8% in terms
of filtration accuracy. Additional tests are also conducted on publicly
available datasets to demonstrate the generalization capabilities of the
proposed algorithm in the presence of illumination variations and different
motion dynamics. Compared to existing solutions, qualitative results verified
the superior capability of the proposed algorithm to eliminate noise while
preserving meaningful scene events
Thermodynamic Performance Investigation of a Small-Scale Solar Compression-Assisted Multi-Ejector Indoor Air Conditioning System for Hot Climate Conditions
In year-round hot climatic conditions, conventional air conditioning systems consume significant amounts of electricity primarily generated by conventional power plants. A compression-assisted, multi-ejector space cooling system driven by low-grade solar thermal energy is investigated in terms of energy and exergy performance, using a real gas property-based ejector model for a 36 kW-scale air conditioning application, exposed to annually high outdoor temperatures (i.e., up to 42 °C), for four working fluids (R11, R141b, R245fa, R600a). Using R245fa, the multi-ejector system effectively triples the operating condenser temperature range of a single ejector system to cover the range of annual outdoor conditions, while compression boosting reduces the generator heat input requirement and improves the overall refrigeration coefficient of performance (COP) by factors of ~3–8 at medium- to high-bound condenser temperatures, relative to simple ejector cycles. The system solar fraction varies from ~0.2 to 0.9 in summer and winter, respectively, with annual average mechanical and overall COPs of 24.5 and 0.21, respectively. Exergy destruction primarily takes place in the ejector assembly, but ejector exergy efficiency improves with compression boosting. The system could reduce annual electric cooling loads by over 40% compared with a conventional local split air conditioner, with corresponding savings in electricity expenditure and GHG emissions
Asynchronous Events-based Panoptic Segmentation using Graph Mixer Neural Network
In the context of robotic grasping, object segmentation encounters several
difficulties when faced with dynamic conditions such as real-time operation,
occlusion, low lighting, motion blur, and object size variability. In response
to these challenges, we propose the Graph Mixer Neural Network that includes a
novel collaborative contextual mixing layer, applied to 3D event graphs formed
on asynchronous events. The proposed layer is designed to spread spatiotemporal
correlation within an event graph at four nearest neighbor levels parallelly.
We evaluate the effectiveness of our proposed method on the Event-based
Segmentation (ESD) Dataset, which includes five unique image degradation
challenges, including occlusion, blur, brightness, trajectory, scale variance,
and segmentation of known and unknown objects. The results show that our
proposed approach outperforms state-of-the-art methods in terms of mean
intersection over the union and pixel accuracy. Code available at:
https://github.com/sanket0707/GNN-Mixer.gitComment: 9 pages, 6 figure