Author: Han-Pang Chiu
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MaAST: Map Attention with Semantic Transformers for Efficient Visual Navigation
Through this work, we design a novel approach that focuses on performing better or comparable to the existing learning-based solutions but under a clear time/computational budget.
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RGB2LIDAR: Towards Solving Large-Scale Cross-Modal Visual Localization
We study an important, yet largely unexplored problem of large-scale cross-modal visual localization by matching ground RGB images to a geo-referenced aerial LIDAR 3D point cloud.
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Semantically-Aware Attentive Neural Embeddings for 2D Long-Term Visual Localization
We present an approach that combines appearance and semantic information for 2D image-based localization (2D-VL) across large perceptual changes and time lags.
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Multi-Sensor Fusion for Motion Estimation in Visually-Degraded Environments
This paper analyzes the feasibility of utilizing multiple low-cost on-board sensors for ground robots or drones navigating in visually-degraded environments.
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Augmented Reality Driving Using Semantic Geo-Registration
We propose a new approach that utilizes semantic information to register 2D monocular video frames to the world using 3D georeferenced data, for augmented reality driving applications.
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Utilizing Semantic Visual Landmarks for Precise Vehicle Navigation
This paper presents a new approach for integrating semantic information for vision-based vehicle navigation.
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Sub-Meter Vehicle Navigation Using Efficient Pre-Mapped Visual Landmarks
This paper presents a vehicle navigation system that is capable of achieving sub-meter GPS-denied navigation accuracy in large-scale urban environments, using pre-mapped visual landmarks.
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Precise Vision-Aided Aerial Navigation
This paper proposes a novel vision-aided navigation approach that continuously estimates precise 3D absolute pose for aerial vehicles, using only inertial measurements and monocular camera observations.
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Virtual Insertion: Robust Bundle Adjustment Over Long Video Sequences
Our goal is to circumvent one of the roadblocks of using existing bundle adjustment algorithms for achieving satisfactory large-area structure from motion over long video sequences, namely, the need for sufficient visual features tracked across consecutive frames.
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Mining Structure Fragments for Smart Bundle Adjustment
In this work we show that, when using conjugate gradient solvers, there is a computational advantage in “grouping” factors corresponding to sets of points (fragments) that are co-visible by the same set of cameras.
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Constrained Optimal Selection for Multi-Sensor Robot Navigation Using Plug-and-Play Factor Graphs
This paper proposes a real-time navigation approach that is able to integrate many sensor types while fulfilling performance needs and system constraints.
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Robust Vision-Aided Navigation Using Sliding-Window Factor Graphs
This paper proposes a navigation algorithm that provides a low-latency solution while estimating the full nonlinear navigation state.