A Constant-Time Efficient Stereo SLAM System

Mei, Christopher; Sibley, Gabe; Cummins, Mark; Newman, Paul; Reid, Ian

doi:10.5244/c.23.54

Cited by 93 publications

(68 citation statements)

References 25 publications

(31 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In addition, we use visual odometry to estimate the camera vehicle's egomotion, and we estimate the scene ground plane in each frame. For both tasks, there are also real-time approaches available [13,15]. For the purpose of this paper, we use the data generously provided with the datasets of [7].…”

Section: Related Workmentioning

confidence: 99%

Real-Time Multi-Person Tracking with Time-Constrained Detection

Mitzel¹,

Sudowe²,

Leibe³

2011

Procedings of the British Machine Vision Conference 2011

View full text Add to dashboard Cite

This paper presents a robust real-time multi-person tracking framework for busy street scenes. Tracking-by-detection approaches have recently been successfully applied to this task. However, their run-time is still limited by the computationally expensive object detection component. In this paper, we therefore consider the problem of making best use of an object detector with a fixed and very small time budget. The question we ask is: given a fixed time budget that allows for detector-based verification of k small regions-of-interest (ROIs) in the image, what are the best regions to attend to in order to obtain stable tracking performance? We address this problem by applying a statistical Poisson process model in order to rate the urgency by which individual ROIs should be attended to. These ROIs are initially extracted from a 3D depth-based occupancy map of the scene and are then tracked over time. This allows us to balance the system resources in order to satisfy the twin goals of detecting newly appearing objects, while maintaining the quality of existing object trajectories.

show abstract

Section: Related Workmentioning

confidence: 99%

Real-Time Multi-Person Tracking with Time-Constrained Detection

Mitzel¹,

Sudowe²,

Leibe³

2011

Procedings of the British Machine Vision Conference 2011

View full text Add to dashboard Cite

show abstract

“…After optimizing the camera poses, BA is performed to optimize both 3D positions of feature points and the camera poses. For the loop closing, a visual information-based approach is employed [35]. They used a bag-of-words-based image retrieval technique to detect one of the keyframes which view is similar with the current view [36].…”

Section: Techniques On Global Map Optimizationmentioning

confidence: 99%

“…In a vSLAM system [35], a stereo camera is selected as a vision sensor. In this case, the scale of the coordinate system is fixed and known.…”

Section: Techniques On Global Map Optimizationmentioning

confidence: 99%

Visual SLAM algorithms: a survey from 2010 to 2016

Taketomi

Uchiyama

Ikeda

2017

IPSJ T Comput Vis Appl

561

274

View full text Add to dashboard Cite

SLAM is an abbreviation for simultaneous localization and mapping, which is a technique for estimating sensor motion and reconstructing structure in an unknown environment. Especially, Simultaneous Localization and Mapping (SLAM) using cameras is referred to as visual SLAM (vSLAM) because it is based on visual information only. vSLAM can be used as a fundamental technology for various types of applications and has been discussed in the field of computer vision, augmented reality, and robotics in the literature. This paper aims to categorize and summarize recent vSLAM algorithms proposed in different research communities from both technical and historical points of views. Especially, we focus on vSLAM algorithms proposed mainly from 2010 to 2016 because major advance occurred in that period. The technical categories are summarized as follows: feature-based, direct, and RGB-D camera-based approaches.

show abstract

“…New features are jointly initialised at keyframes where FAST [26] produces candidate locations. In order to ensure an equal distribution of feature locations over the image, we employ a quadtree similar to Mei et al [18]. Given the FAST feature location z = (u, v) ⊤ , we set y = (u, v, q) with q ∈ R + .…”

Section: Feature Initialisationmentioning

confidence: 99%

“…Probably the most complete is that of Mei et al [18], which combines robust and accurate local visual odometry with constant-time large-scale mapping (enabled by a continuous relative map representation), highperformance appearance-based loop closure detection, and global metric map optimisation if required. Another similar system is that presented by Konolige and Agrawal [16].…”

Section: Introductionmentioning

confidence: 99%

Scale Drift-Aware Large Scale Monocular SLAM

Strasdat¹,

Montiel²,

Davison³

2010

Robotics: Science and Systems VI

357

309

View full text Add to dashboard Cite

Abstract-State of the art visual SLAM systems have recently been presented which are capable of accurate, large-scale and real-time performance, but most of these require stereo vision. Important application areas in robotics and beyond open up if similar performance can be demonstrated using monocular vision, since a single camera will always be cheaper, more compact and easier to calibrate than a multi-camera rig.With high quality estimation, a single camera moving through a static scene of course effectively provides its own stereo geometry via frames distributed over time. However, a classic issue with monocular visual SLAM is that due to the purely projective nature of a single camera, motion estimates and map structure can only be recovered up to scale. Without the known inter-camera distance of a stereo rig to serve as an anchor, the scale of locally constructed map portions and the corresponding motion estimates is therefore liable to drift over time.In this paper we describe a new near real-time visual SLAM system which adopts the continuous keyframe optimisation approach of the best current stereo systems, but accounts for the additional challenges presented by monocular input. In particular, we present a new pose-graph optimisation technique which allows for the efficient correction of rotation, translation and scale drift at loop closures. Especially, we describe the Lie group of similarity transformations and its relation to the corresponding Lie algebra. We also present in detail the system's new image processing front-end which is able accurately to track hundreds of features per frame, and a filter-based approach for feature initialisation within keyframe-based SLAM. Our approach is proven via large-scale simulation and real-world experiments where a camera completes large looped trajectories.

show abstract

A Constant-Time Efficient Stereo SLAM System

Cited by 93 publications

References 25 publications

Real-Time Multi-Person Tracking with Time-Constrained Detection

Real-Time Multi-Person Tracking with Time-Constrained Detection

Visual SLAM algorithms: a survey from 2010 to 2016

Scale Drift-Aware Large Scale Monocular SLAM

Contact Info

Product

Resources

About