Mapping with depth panoramas

Taylor, Camillo J.; Cowley, Anthony; Kettler, Rafe; Ninomiya, Kai; Gupta, Mona; Niu, Boyang

doi:10.1109/iros.2015.7354271

Cited by 12 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…LLOL [11] uses spinning lidar as a streaming sensor through lidar points division and avoids the problem of insufficient constraints on partial scans by guaranteeing a complete frame of data is used for registration through a circular buffer. Besides, LLOL organizes the map points in the form of depth panorama [15] to ensure the system's efficiency. Because it needs to estimate the pose by the amount of data in the complete frame when processing each partial scan, the system becomes less efficient as the number of partial scans increases.…”

Section: A Lidar-inertial Odometrymentioning

confidence: 99%

FR-LIO: Fast and Robust Lidar-Inertial Odometry by Tightly-Coupled Iterated Kalman Smoother and Robocentric Voxels

Zhao¹,

Qian²,

Zhang³

et al. 2023

Preprint

View full text Add to dashboard Cite

This paper presents a fast lidar-inertial odometry (LIO) system that is robust to aggressive motion. To achieve robust tracking in aggressive motion scenes, we exploit the continuous scanning property of lidar to adaptively divide the full scan into multiple partial scans (named subframes) according to the motion intensity. And to avoid the degradation of sub-frames resulting from insufficient constraints, we propose a robust state estimation method based on a tightly-coupled iterated error state Kalman smoother (ESKS) framework. Furthermore, we propose a robocentric voxel map (RC-Vox) to improve the system's efficiency. The RC-Vox allows efficient maintenance of map points and k nearest neighbor (k-NN) queries by mapping local map points into a fixed-size, two-layer 3D array structure. Extensive experiments were conducted on 27 sequences from 4 public datasets and our own dataset. The results show that our system can achieve stable tracking in aggressive motion scenes that cannot be handled by other state-of-the-art methods, while our system can achieve competitive performance with these methods in general scenes. In terms of efficiency, the RC-Vox allows our system to achieve the fastest speed compared with the current advanced LIO systems.

show abstract

Section: A Lidar-inertial Odometrymentioning

confidence: 99%

FR-LIO: Fast and Robust Lidar-Inertial Odometry by Tightly-Coupled Iterated Kalman Smoother and Robocentric Voxels

Zhao¹,

Qian²,

Zhang³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The local maps support small-scale loop closures by virtue of powering local frame-to-model tracking in the spirit of KinectFusion [14], while the pose graph enables large-scale loop closures. The local map representation is a panoramic keyframe [15], [16] recording depth, surface normal, and confidence for each pixel, Fig. 7.…”

Section: Mappingmentioning

confidence: 99%

Mine Tunnel Exploration Using Multiple Quadrupedal Robots

Miller

Cohen

Kulkarni

et al. 2020

IEEE Robot. Autom. Lett.

Self Cite

View full text Add to dashboard Cite

Robotic exploration of underground environments is a particularly challenging problem due to communication, endurance, and traversability constraints which necessitate high degrees of autonomy and agility. These challenges are further enhanced by the need to minimize human intervention for practical applications. While legged robots have the ability to traverse extremely challenging terrain, they also engender further inherent challenges for planning, estimation, and control.In this work, we describe a fully autonomous system for multi-robot mine exploration and mapping using legged quadrupeds, as well as a distributed database mesh networking system for reporting data. In addition, we show results from the DARPA Subterranean Challenge (SubT) Tunnel Circuit demonstrating localization of artifacts after traversals of hundreds of meters. To our knowledge, these experiments represent the first fully autonomous exploration of an unknown GNSS-denied environment undertaken by legged robots.

show abstract

“…Fast-LIO2 achieves real-performance on embedded CPUs via the use of an incremental KD Tree and tightly-couples IMU data. Perhaps the most similar work to ours is UPSLAM [22], which uses a union of depth panoramas [23] as its map representation. It is also a full-fledged SLAM system that includes loop closure.…”

Section: B Lidar-inertial Odometrymentioning

confidence: 99%

“…Following UPSLAM [22], we employ a depth panorama (pano) [23] as our local map. A pano is similar to a sweep, but with higher resolution and represents the 3D structure at one point along the sensors trajectory.…”

Section: B Local Map Representationmentioning

confidence: 99%

LLOL: Low-Latency Odometry for Spinning Lidars

Qu¹,

Shivakumar²,

Liu³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

In this paper, we present a low-latency odometry system designed for spinning lidars. Many existing lidar odometry methods wait for an entire sweep from the lidar before processing the data. This introduces a large delay between the first laser firing and its pose estimate. To reduce this latency, we treat the spinning lidar as a streaming sensor and process packets as they arrive. This effectively distributes expensive operations across time, resulting in a very fast and lightweight system with a much higher throughput and lower latency. Our open source implementation is available at https: //github.com/versatran01/llol.

show abstract

Mapping with depth panoramas

Cited by 12 publications

References 10 publications

FR-LIO: Fast and Robust Lidar-Inertial Odometry by Tightly-Coupled Iterated Kalman Smoother and Robocentric Voxels

FR-LIO: Fast and Robust Lidar-Inertial Odometry by Tightly-Coupled Iterated Kalman Smoother and Robocentric Voxels

Mine Tunnel Exploration Using Multiple Quadrupedal Robots

LLOL: Low-Latency Odometry for Spinning Lidars

Contact Info

Product

Resources

About