Unlocking the Performance of Proximity Sensors by Utilizing Transient Histograms

• URL: http://arxiv.org/abs/2308.13473v1
• Date: Fri, 25 Aug 2023 16:20:41 GMT
• Title: Unlocking the Performance of Proximity Sensors by Utilizing Transient Histograms
• Authors: Carter Sifferman, Yeping Wang, Mohit Gupta, and Michael Gleicher
• Abstract summary: We provide methods which recover planar scene geometry by utilizing the transient histograms captured by a class of close-range time-of-flight (ToF) distance sensor. A transient histogram is a one dimensional temporal waveform which encodes the arrival time of photons incident on the ToF sensor. We demonstrate a simple robotics application which uses our method to sense the distance to and slope of a planar surface from a sensor mounted on the end effector of a robot arm.
• Score: 20.994250740256458
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We provide methods which recover planar scene geometry by utilizing the transient histograms captured by a class of close-range time-of-flight (ToF) distance sensor. A transient histogram is a one dimensional temporal waveform which encodes the arrival time of photons incident on the ToF sensor. Typically, a sensor processes the transient histogram using a proprietary algorithm to produce distance estimates, which are commonly used in several robotics applications. Our methods utilize the transient histogram directly to enable recovery of planar geometry more accurately than is possible using only proprietary distance estimates, and consistent recovery of the albedo of the planar surface, which is not possible with proprietary distance estimates alone. This is accomplished via a differentiable rendering pipeline, which simulates the transient imaging process, allowing direct optimization of scene geometry to match observations. To validate our methods, we capture 3,800 measurements of eight planar surfaces from a wide range of viewpoints, and show that our method outperforms the proprietary-distance-estimate baseline by an order of magnitude in most scenarios. We demonstrate a simple robotics application which uses our method to sense the distance to and slope of a planar surface from a sensor mounted on the end effector of a robot arm.

Related papers

• Real-Time Polygonal Semantic Mapping for Humanoid Robot Stair Climbing [19.786955745157453]
  We present a novel algorithm for real-time planar semantic mapping tailored for humanoid robots navigating complex terrains such as staircases. We utilize an anisotropic diffusion filter on depth images to effectively minimize noise from gradient jumps while preserving essential edge details. Our approach achieves real-time performance, processing single frames at rates exceeding $30Hz$, which facilitates detailed plane extraction and map management swiftly and efficiently.
  arXiv  Detail & Related papers  (2024-11-04T09:34:55Z)
• Using a Distance Sensor to Detect Deviations in a Planar Surface [20.15053198469424]
  We investigate methods for determining if a planar surface contains geometric deviations using only an instantaneous measurement from a miniature optical time-of-flight sensor. Key to our method is to utilize the entirety of information encoded in raw time-of-flight data captured by off-the-shelf distance sensors. We build an example application in which our method enables mobile robot obstacle avoidance over a wide field-of-view.
  arXiv  Detail & Related papers  (2024-08-07T15:24:25Z)
• RFTrans: Leveraging Refractive Flow of Transparent Objects for Surface Normal Estimation and Manipulation [50.10282876199739]
  This paper introduces RFTrans, an RGB-D-based method for surface normal estimation and manipulation of transparent objects. It integrates the RFNet, which predicts refractive flow, object mask, and boundaries, followed by the F2Net, which estimates surface normal from the refractive flow. A real-world robot grasping task witnesses an 83% success rate, proving that refractive flow can help enable direct sim-to-real transfer.
  arXiv  Detail & Related papers  (2023-11-21T07:19:47Z)
• Vanishing Point Estimation in Uncalibrated Images with Prior Gravity Direction [82.72686460985297]
  We tackle the problem of estimating a Manhattan frame. We derive two new 2-line solvers, one of which does not suffer from singularities affecting existing solvers. We also design a new non-minimal method, running on an arbitrary number of lines, to boost the performance in local optimization.
  arXiv  Detail & Related papers  (2023-08-21T13:03:25Z)
• Fast Monocular Scene Reconstruction with Global-Sparse Local-Dense Grids [84.90863397388776]
  We propose to directly use signed distance function (SDF) in sparse voxel block grids for fast and accurate scene reconstruction without distances. Our globally sparse and locally dense data structure exploits surfaces' spatial sparsity, enables cache-friendly queries, and allows direct extensions to multi-modal data. Experiments show that our approach is 10x faster in training and 100x faster in rendering while achieving comparable accuracy to state-of-the-art neural implicit methods.
  arXiv  Detail & Related papers  (2023-05-22T16:50:19Z)
• Real-Time Simultaneous Localization and Mapping with LiDAR intensity [9.374695605941627]
  We propose a novel real-time LiDAR intensity image-based simultaneous localization and mapping method. Our method can run in real time with high accuracy and works well with illumination changes, low-texture, and unstructured environments.
  arXiv  Detail & Related papers  (2023-01-23T03:59:48Z)
• A Distance-Geometric Method for Recovering Robot Joint Angles From an RGB Image [7.971699294672282]
  We present a novel method for retrieving the joint angles of a robot manipulator using only a single RGB image of its current configuration. Our approach, based on a distance-geometric representation of the configuration space, exploits the knowledge of a robot's kinematic model.
  arXiv  Detail & Related papers  (2023-01-05T12:57:45Z)
• iSDF: Real-Time Neural Signed Distance Fields for Robot Perception [64.80458128766254]
  iSDF is a continuous learning system for real-time signed distance field reconstruction. It produces more accurate reconstructions and better approximations of collision costs and gradients.
  arXiv  Detail & Related papers  (2022-04-05T15:48:39Z)
• Soft Expectation and Deep Maximization for Image Feature Detection [68.8204255655161]
  We propose SEDM, an iterative semi-supervised learning process that flips the question and first looks for repeatable 3D points, then trains a detector to localize them in image space. Our results show that this new model trained using SEDM is able to better localize the underlying 3D points in a scene.
  arXiv  Detail & Related papers  (2021-04-21T00:35:32Z)
• Unified Multi-Modal Landmark Tracking for Tightly Coupled Lidar-Visual-Inertial Odometry [5.131684964386192]
  We present an efficient multi-sensor odometry system for mobile platforms that jointly optimize visual, lidar, and inertial information. New method to extract 3D line and planar primitives from lidar point clouds is presented. System has been tested on a variety of platforms and scenarios, including underground exploration with a legged robot and outdoor scanning with a dynamically moving handheld device.
  arXiv  Detail & Related papers  (2020-11-13T09:54:03Z)
• Lightweight Multi-View 3D Pose Estimation through Camera-Disentangled Representation [57.11299763566534]
  We present a solution to recover 3D pose from multi-view images captured with spatially calibrated cameras. We exploit 3D geometry to fuse input images into a unified latent representation of pose, which is disentangled from camera view-points. Our architecture then conditions the learned representation on camera projection operators to produce accurate per-view 2d detections.
  arXiv  Detail & Related papers  (2020-04-05T12:52:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.