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Found 8 papers, 2 papers with code
Exploring the Physical World Adversarial Robustness of Vehicle Detection
Through this pipeline, we establish the Discrete and Continuous Instant-level (DCI) dataset, enabling comprehensive experiments involving three detection models and three physical adversarial attacks.
The Digital Divide in Canada and the Role of LEO Satellites in Bridging the Gap
In 2016, the Canadian Radio-television and Telecommunications Commission announced broadband Internet as a basic service available for all Canadians.
Fisher Information Field: an Efficient and Differentiable Map for Perception-aware Planning
However, computing the Fisher information from a set of sparse landmarks (i. e., a point cloud), which is the most common map for visual localization, is inefficient.
Robotics
Reference Pose Generation for Long-term Visual Localization via Learned Features and View Synthesis
Visual Localization is one of the key enabling technologies for autonomous driving and augmented reality.
Voxel Map for Visual SLAM
We propose a voxel-map representation to efficiently retrieve map points for visual SLAM.
Redesigning SLAM for Arbitrary Multi-Camera Systems
We adapt a state-of-the-art visual-inertial odometry with these modifications, and experimental results show that the modified pipeline can adapt to a wide range of camera setups (e. g., 2 to 6 cameras in one experiment) without the need of sensor-specific modifications or tuning.
Visual-Inertial Odometry of Aerial Robots
Visual-Inertial odometry (VIO) is the process of estimating the state (pose and velocity) of an agent (e. g., an aerial robot) by using only the input of one or more cameras plus one or more Inertial Measurement Units (IMUs) attached to it.
Robotics
Learning-based Image Enhancement for Visual Odometry in Challenging HDR Environments
One of the main open challenges in visual odometry (VO) is the robustness to difficult illumination conditions or high dynamic range (HDR) environments.
