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pro vyhledávání: '"Saripalli A"'
Autor:
Arias-Perez, Pedro, Gautam, Alvika, Fernandez-Cortizas, Miguel, Perez-Saura, David, Saripalli, Srikanth, Campoy, Pascual
Recent advances have improved autonomous navigation and mapping under payload constraints, but current multi-robot inspection algorithms are unsuitable for nano-drones due to their need for heavy sensors and high computational resources. To address t
Externí odkaz:
http://arxiv.org/abs/2407.06706
Autor:
Maesumi, Arman, Hu, Dylan, Saripalli, Krishi, Kim, Vladimir G., Fisher, Matthew, Pirk, Sören, Ritchie, Daniel
Procedural noise is a fundamental component of computer graphics pipelines, offering a flexible way to generate textures that exhibit "natural" random variation. Many different types of noise exist, each produced by a separate algorithm. In this pape
Externí odkaz:
http://arxiv.org/abs/2404.16292
LiDAR semantic segmentation frameworks predominantly use geometry-based features to differentiate objects within a scan. Although these methods excel in scenarios with clear boundaries and distinct shapes, their performance declines in environments w
Externí odkaz:
http://arxiv.org/abs/2403.13188
This paper presents a novel system designed for 3D mapping and visual relocalization using 3D Gaussian Splatting. Our proposed method uses LiDAR and camera data to create accurate and visually plausible representations of the environment. By leveragi
Externí odkaz:
http://arxiv.org/abs/2403.11367
LiDAR is used in autonomous driving to provide 3D spatial information and enable accurate perception in off-road environments, aiding in obstacle detection, mapping, and path planning. Learning-based LiDAR semantic segmentation utilizes machine learn
Externí odkaz:
http://arxiv.org/abs/2401.01439
Predicting pedestrian behavior is the key to ensure safety and reliability of autonomous vehicles. While deep learning methods have been promising by learning from annotated video frame sequences, they often fail to fully grasp the dynamic interactio
Externí odkaz:
http://arxiv.org/abs/2311.14786
Autor:
Overbye, Timothy, Saripalli, Srikanth
Off-road robotics have traditionally utilized lidar for local navigation due to its accuracy and high resolution. However, the limitations of lidar, such as reduced performance in harsh environmental conditions and limited range, have prompted the ex
Externí odkaz:
http://arxiv.org/abs/2310.17620
Autor:
Jiang, Peng, Saripalli, Srikanth
As the demand for autonomous navigation in off-road environments increases, the need for effective solutions to understand these surroundings becomes essential. In this study, we confront the inherent complexities of semantic segmentation in RADAR da
Externí odkaz:
http://arxiv.org/abs/2310.13551
Autor:
Hartzer, Jacob, Saripalli, Srikanth
This work presents a centralized multi-IMU filter framework with online intrinsic and extrinsic calibration for unsynchronized inertial measurement units that is robust against changes in calibration parameters. The novel EKF-based method estimates t
Externí odkaz:
http://arxiv.org/abs/2310.12411
Robust and accurate tracking and localization of road users like pedestrians and cyclists is crucial to ensure safe and effective navigation of Autonomous Vehicles (AVs), particularly so in urban driving scenarios with complex vehicle-pedestrian inte
Externí odkaz:
http://arxiv.org/abs/2309.16057