The Experts below are selected from a list of 281943 Experts worldwide ranked by ideXlab platform
Jua Nieto - One of the best experts on this subject based on the ideXlab platform.
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freetures localization in signed Distance Function maps
arXiv: Robotics, 2020Co-Authors: Alexande Millane, Hele Oleynikova, Christia Lanegge, Jeff Delmerico, Jua Nieto, Roland Siegwa, Marc Pollefeys, Cesa CadenaAbstract:Localization of a robotic system within a previously mapped environment is important for reducing estimation drift and for reusing previously built maps. Existing techniques for geometry-based localization have focused on the description of local surface geometry, usually using pointclouds as the underlying representation. We propose a system for geometry-based localization that extracts features directly from an implicit surface representation: the Signed Distance Function (SDF). The SDF varies continuously through space, which allows the proposed system to extract and utilize features describing both surfaces and free-space. Through evaluations on public datasets, we demonstrate the flexibility of this approach, and show an increase in localization performance over state-of-the-art handcrafted surfaces-only descriptors. We achieve an average improvement of ~12% on an RGB-D dataset and ~18% on a LiDAR-based dataset. Finally, we demonstrate our system for localizing a LiDAR-equipped MAV within a previously built map of a search and rescue training ground.
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voxgraph globally consistent volumetric mapping using signed Distance Function submaps
International Conference on Robotics and Automation, 2020Co-Authors: Victo Reijgwa, Alexande Millane, Hele Oleynikova, Roland Siegwa, Cesa Cadena, Jua NietoAbstract:Globally consistent dense maps are a key requirement for long-term robot navigation in complex environments. While previous works have addressed the challenges of dense mapping and global consistency, most require more computational resources than may be available on-board small robots. We propose a framework that creates globally consistent volumetric maps on a CPU and is lightweight enough to run on computationally constrained platforms. Our approach represents the environment as a collection of overlapping signed Distance Function (SDF) submaps and maintains global consistency by computing an optimal alignment of the submap collection. By exploiting the underlying SDF representation, we generate correspondence-free constraints between submap pairs that are computationally efficient enough to optimize the global problem each time a new submap is added. We deploy the proposed system on a hexacopter micro aerial vehicle (MAV) with an Intel i7-8650 U CPU in two realistic scenarios: mapping a large-scale area using a 3D LiDAR and mapping an industrial space using an RGB-D camera. In the large-scale outdoor experiments, the system optimizes a 120 × 80 m map in less than 4 s and produces absolute trajectory RMSEs of less than 1 m over 400 m trajectories. Our complete system, called voxgraph , is available as open source. 1 1 https://github.com/ethz-asl/voxgraph .
Marc Pollefeys - One of the best experts on this subject based on the ideXlab platform.
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freetures localization in signed Distance Function maps
arXiv: Robotics, 2020Co-Authors: Alexande Millane, Hele Oleynikova, Christia Lanegge, Jeff Delmerico, Jua Nieto, Roland Siegwa, Marc Pollefeys, Cesa CadenaAbstract:Localization of a robotic system within a previously mapped environment is important for reducing estimation drift and for reusing previously built maps. Existing techniques for geometry-based localization have focused on the description of local surface geometry, usually using pointclouds as the underlying representation. We propose a system for geometry-based localization that extracts features directly from an implicit surface representation: the Signed Distance Function (SDF). The SDF varies continuously through space, which allows the proposed system to extract and utilize features describing both surfaces and free-space. Through evaluations on public datasets, we demonstrate the flexibility of this approach, and show an increase in localization performance over state-of-the-art handcrafted surfaces-only descriptors. We achieve an average improvement of ~12% on an RGB-D dataset and ~18% on a LiDAR-based dataset. Finally, we demonstrate our system for localizing a LiDAR-equipped MAV within a previously built map of a search and rescue training ground.
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dist rendering deep implicit signed Distance Function with differentiable sphere tracing
Computer Vision and Pattern Recognition, 2020Co-Authors: Yinda Zhang, Songyou Peng, Marc PollefeysAbstract:We propose a differentiable sphere tracing algorithm to bridge the gap between inverse graphics methods and the recently proposed deep learning based implicit signed Distance Function. Due to the nature of the implicit Function, the rendering process requires tremendous Function queries, which is particularly problematic when the Function is represented as a neural network. We optimize both the forward and backward pass of our rendering layer to make it run efficiently with affordable memory consumption on a commodity graphics card. Our rendering method is fully differentiable such that losses can be directly computed on the rendered 2D observations, and the gradients can be propagated backward to optimize the 3D geometry. We show that our rendering method can effectively reconstruct accurate 3D shapes from various inputs, such as sparse depth and multi-view images, through inverse optimization. With the geometry based reasoning, our 3D shape prediction methods show excellent generalization capability and robustness against various noises.
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dist rendering deep implicit signed Distance Function with differentiable sphere tracing
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Yinda Zhang, Songyou Peng, Marc PollefeysAbstract:We propose a differentiable sphere tracing algorithm to bridge the gap between inverse graphics methods and the recently proposed deep learning based implicit signed Distance Function. Due to the nature of the implicit Function, the rendering process requires tremendous Function queries, which is particularly problematic when the Function is represented as a neural network. We optimize both the forward and backward passes of our rendering layer to make it run efficiently with affordable memory consumption on a commodity graphics card. Our rendering method is fully differentiable such that losses can be directly computed on the rendered 2D observations, and the gradients can be propagated backwards to optimize the 3D geometry. We show that our rendering method can effectively reconstruct accurate 3D shapes from various inputs, such as sparse depth and multi-view images, through inverse optimization. With the geometry based reasoning, our 3D shape prediction methods show excellent generalization capability and robustness against various noises.
Cesa Cadena - One of the best experts on this subject based on the ideXlab platform.
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freetures localization in signed Distance Function maps
arXiv: Robotics, 2020Co-Authors: Alexande Millane, Hele Oleynikova, Christia Lanegge, Jeff Delmerico, Jua Nieto, Roland Siegwa, Marc Pollefeys, Cesa CadenaAbstract:Localization of a robotic system within a previously mapped environment is important for reducing estimation drift and for reusing previously built maps. Existing techniques for geometry-based localization have focused on the description of local surface geometry, usually using pointclouds as the underlying representation. We propose a system for geometry-based localization that extracts features directly from an implicit surface representation: the Signed Distance Function (SDF). The SDF varies continuously through space, which allows the proposed system to extract and utilize features describing both surfaces and free-space. Through evaluations on public datasets, we demonstrate the flexibility of this approach, and show an increase in localization performance over state-of-the-art handcrafted surfaces-only descriptors. We achieve an average improvement of ~12% on an RGB-D dataset and ~18% on a LiDAR-based dataset. Finally, we demonstrate our system for localizing a LiDAR-equipped MAV within a previously built map of a search and rescue training ground.
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voxgraph globally consistent volumetric mapping using signed Distance Function submaps
International Conference on Robotics and Automation, 2020Co-Authors: Victo Reijgwa, Alexande Millane, Hele Oleynikova, Roland Siegwa, Cesa Cadena, Jua NietoAbstract:Globally consistent dense maps are a key requirement for long-term robot navigation in complex environments. While previous works have addressed the challenges of dense mapping and global consistency, most require more computational resources than may be available on-board small robots. We propose a framework that creates globally consistent volumetric maps on a CPU and is lightweight enough to run on computationally constrained platforms. Our approach represents the environment as a collection of overlapping signed Distance Function (SDF) submaps and maintains global consistency by computing an optimal alignment of the submap collection. By exploiting the underlying SDF representation, we generate correspondence-free constraints between submap pairs that are computationally efficient enough to optimize the global problem each time a new submap is added. We deploy the proposed system on a hexacopter micro aerial vehicle (MAV) with an Intel i7-8650 U CPU in two realistic scenarios: mapping a large-scale area using a 3D LiDAR and mapping an industrial space using an RGB-D camera. In the large-scale outdoor experiments, the system optimizes a 120 × 80 m map in less than 4 s and produces absolute trajectory RMSEs of less than 1 m over 400 m trajectories. Our complete system, called voxgraph , is available as open source. 1 1 https://github.com/ethz-asl/voxgraph .
Victo Reijgwa - One of the best experts on this subject based on the ideXlab platform.
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voxgraph globally consistent volumetric mapping using signed Distance Function submaps
International Conference on Robotics and Automation, 2020Co-Authors: Victo Reijgwa, Alexande Millane, Hele Oleynikova, Roland Siegwa, Cesa Cadena, Jua NietoAbstract:Globally consistent dense maps are a key requirement for long-term robot navigation in complex environments. While previous works have addressed the challenges of dense mapping and global consistency, most require more computational resources than may be available on-board small robots. We propose a framework that creates globally consistent volumetric maps on a CPU and is lightweight enough to run on computationally constrained platforms. Our approach represents the environment as a collection of overlapping signed Distance Function (SDF) submaps and maintains global consistency by computing an optimal alignment of the submap collection. By exploiting the underlying SDF representation, we generate correspondence-free constraints between submap pairs that are computationally efficient enough to optimize the global problem each time a new submap is added. We deploy the proposed system on a hexacopter micro aerial vehicle (MAV) with an Intel i7-8650 U CPU in two realistic scenarios: mapping a large-scale area using a 3D LiDAR and mapping an industrial space using an RGB-D camera. In the large-scale outdoor experiments, the system optimizes a 120 × 80 m map in less than 4 s and produces absolute trajectory RMSEs of less than 1 m over 400 m trajectories. Our complete system, called voxgraph , is available as open source. 1 1 https://github.com/ethz-asl/voxgraph .
Alexande Millane - One of the best experts on this subject based on the ideXlab platform.
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freetures localization in signed Distance Function maps
arXiv: Robotics, 2020Co-Authors: Alexande Millane, Hele Oleynikova, Christia Lanegge, Jeff Delmerico, Jua Nieto, Roland Siegwa, Marc Pollefeys, Cesa CadenaAbstract:Localization of a robotic system within a previously mapped environment is important for reducing estimation drift and for reusing previously built maps. Existing techniques for geometry-based localization have focused on the description of local surface geometry, usually using pointclouds as the underlying representation. We propose a system for geometry-based localization that extracts features directly from an implicit surface representation: the Signed Distance Function (SDF). The SDF varies continuously through space, which allows the proposed system to extract and utilize features describing both surfaces and free-space. Through evaluations on public datasets, we demonstrate the flexibility of this approach, and show an increase in localization performance over state-of-the-art handcrafted surfaces-only descriptors. We achieve an average improvement of ~12% on an RGB-D dataset and ~18% on a LiDAR-based dataset. Finally, we demonstrate our system for localizing a LiDAR-equipped MAV within a previously built map of a search and rescue training ground.
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voxgraph globally consistent volumetric mapping using signed Distance Function submaps
International Conference on Robotics and Automation, 2020Co-Authors: Victo Reijgwa, Alexande Millane, Hele Oleynikova, Roland Siegwa, Cesa Cadena, Jua NietoAbstract:Globally consistent dense maps are a key requirement for long-term robot navigation in complex environments. While previous works have addressed the challenges of dense mapping and global consistency, most require more computational resources than may be available on-board small robots. We propose a framework that creates globally consistent volumetric maps on a CPU and is lightweight enough to run on computationally constrained platforms. Our approach represents the environment as a collection of overlapping signed Distance Function (SDF) submaps and maintains global consistency by computing an optimal alignment of the submap collection. By exploiting the underlying SDF representation, we generate correspondence-free constraints between submap pairs that are computationally efficient enough to optimize the global problem each time a new submap is added. We deploy the proposed system on a hexacopter micro aerial vehicle (MAV) with an Intel i7-8650 U CPU in two realistic scenarios: mapping a large-scale area using a 3D LiDAR and mapping an industrial space using an RGB-D camera. In the large-scale outdoor experiments, the system optimizes a 120 × 80 m map in less than 4 s and produces absolute trajectory RMSEs of less than 1 m over 400 m trajectories. Our complete system, called voxgraph , is available as open source. 1 1 https://github.com/ethz-asl/voxgraph .
