Search Results for author:
Found 17 papers, 1 papers with code
Z-Splat: Z-Axis Gaussian Splatting for Camera-Sonar Fusion
In this manuscript, we demonstrate that using transient data (from sonars) allows us to address the missing cone problem by sampling high-frequency data along the depth axis.
AONeuS: A Neural Rendering Framework for Acoustic-Optical Sensor Fusion
Underwater perception and 3D surface reconstruction are challenging problems with broad applications in construction, security, marine archaeology, and environmental monitoring.
Neural feels with neural fields: Visuo-tactile perception for in-hand manipulation
Our neural representation driven by multimodal sensing can serve as a perception backbone towards advancing robot dexterity.
SONIC: Sonar Image Correspondence using Pose Supervised Learning for Imaging Sonars
In this paper, we address the challenging problem of data association for underwater SLAM through a novel method for sonar image correspondence using learned features.
Neural Radiance Field with LiDAR maps
We address outdoor Neural Radiance Fields (NeRF) with LiDAR maps.
TartanCalib: Iterative Wide-Angle Lens Calibration using Adaptive SubPixel Refinement of AprilTags
An accurate calibration of the intrinsics and extrinsics is a critical pre-requisite for using the edge of a wide-angle lens for depth perception and odometry.
Neural Implicit Surface Reconstruction using Imaging Sonar
We present a technique for dense 3D reconstruction of objects using an imaging sonar, also known as forward-looking sonar (FLS).
Long-term Visual Map Sparsification with Heterogeneous GNN
We address the problem of map sparsification for long-term visual localization.
Revisiting LiDAR Registration and Reconstruction: A Range Image Perspective
We further collect a dataset of indoor and outdoor LiDAR scenes in the posed range image format.
ASH: A Modern Framework for Parallel Spatial Hashing in 3D Perception
Unlike existing GPU hash maps, the ASH framework provides a versatile tensor interface, hiding low-level details from the users.
Learning Tactile Models for Factor Graph-based Estimation
In order to incorporate tactile measurements in the graph, we need local observation models that can map high-dimensional tactile images onto a low-dimensional state space.
Compositional Scalable Object SLAM
We present a fast, scalable, and accurate Simultaneous Localization and Mapping (SLAM) system that represents indoor scenes as a graph of objects.
An Efficient Planar Bundle Adjustment Algorithm
In contrast, a depth sensor can record hundreds of points in a plane at a time, which results in a very large nonlinear least-squares problem even for a small-scale space.
Do not Omit Local Minimizer: a Complete Solution for Pose Estimation from 3D Correspondences
Besides, when the global minimizer is the solution, our algorithm achieves the same accuracy as previous algorithms that have guaranteed global optimality, but our algorithm is applicable to real-time applications.
Unsupervised Learning of Monocular Depth Estimation with Bundle Adjustment, Super-Resolution and Clip Loss
In this paper, we introduce a bundle adjustment framework and a super-resolution network to solve the above two problems.
Robust Keyframe-based Dense SLAM with an RGB-D Camera
In this paper, we present RKD-SLAM, a robust keyframe-based dense SLAM approach for an RGB-D camera that can robustly handle fast motion and dense loop closure, and run without time limitation in a moderate size scene.
Pop-up SLAM: Semantic Monocular Plane SLAM for Low-texture Environments
In this paper, we propose real-time monocular plane SLAM to demonstrate that scene understanding could improve both state estimation and dense mapping especially in low-texture environments.
