Network Uncertainty Informed Semantic Feature Selection for Visual SLAM
In order to facilitate long-term localization using a visual simultaneous localization and mapping (SLAM) algorithm, careful feature selection can help ensure that reference points persist over long durations and the runtime and storage complexity of the algorithm remain consistent. We present SIVO (Semantically Informed Visual Odometry and Mapping), a novel information-theoretic feature selection method for visual SLAM which incorporates semantic segmentation and neural network uncertainty into the feature selection pipeline. Our algorithm selects points which provide the highest reduction in Shannon entropy between the entropy of the current state and the joint entropy of the state, given the addition of the new feature with the classification entropy of the feature from a Bayesian neural network. Each selected feature significantly reduces the uncertainty of the vehicle state and has been detected to be a static object (building, traffic sign, etc.) repeatedly with a high confidence. This selection strategy generates a sparse map which can facilitate long-term localization. The KITTI odometry dataset is used to evaluate our method, and we also compare our results against ORB_SLAM2. Overall, SIVO performs comparably to the baseline method while reducing the map size by almost 70%.
Code (1)
Tasks
feature selectionSemantic SegmentationSimultaneous Localization and MappingVisual OdometrySimilar Papers 제목 키워드 기반
Uncertainty-Aware Deep Neural Representations for Visual Analysis of Vector Field Data
The widespread use of Deep Neural Networks (DNNs) has recently resulted in their application to challenging scientific visualization tasks. While advanced DNNs demonstrate impressive generalization abilities, understandi…
PredictionUncertainty-Informed Volume Visualization using Implicit Neural Representation
The increasing adoption of Deep Neural Networks (DNNs) has led to their application in many challenging scientific visualization tasks. While advanced DNNs offer impressive generalization capabilities, understanding fact…
PredictionEvidMTL: Evidential Multi-Task Learning for Uncertainty-Aware Semantic Surface Mapping from Monocular RGB Images
For scene understanding in unstructured environments, an accurate and uncertainty-aware metric-semantic mapping is required to enable informed action selection by autonomous systems. Existing mapping methods often suffer…
Depth EstimationDepth PredictionMulti-Task LearningScene Understanding+2Hard to See, Hard to Label: Generative and Symbolic Acquisition for Subtle Visual Phenomena
Subtle visual anomalies such as hairline cracks, sub-millimeter voids, and low-contrast inclusions are structurally atypical yet visually ambiguous, making them both difficult to annotate and easy to overlook during acti…
Object DetectionActive LearningCREST: Cross-modal Resonance through Evidential Deep Learning for Enhanced Zero-Shot Learning
Zero-shot learning (ZSL) enables the recognition of novel classes by leveraging semantic knowledge transfer from known to unknown categories. This knowledge, typically encapsulated in attribute descriptions, aids in iden…
AttributeTransfer LearningVisual LocalizationZero-Shot Learning