How far is SLAM from a linear least squares problem?

Huang, Shoudong; Lai, Yingwu; Frese, Udo; Dissanayake, Gamini

doi:10.1109/iros.2010.5652603

Cited by 19 publications

(4 citation statements)

References 14 publications

(15 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Robustness In presence of noise from several sources in the SLAM pipeline, it is sometimes hard for the estimation algorithm to generate optimum estimates of the map and trajectory. Very limited research work has been done to guarantee the optimality of a SLAM estimate or at least verify whether or not the estimate is optimal [17][18][19][55][56][57]. To that end, post-processing SLAM estimates by means of a neural network, for example, might result in significant improvements to the estimated trajectory and reconstructed map, and hence a more robust SLAM system.…”

Section: Resultsmentioning

confidence: 99%

Feature-based visual simultaneous localization and mapping: a survey

Azzam

Taha²,

Huang

et al. 2020

SN Appl. Sci.

View full text Add to dashboard Cite

Visual simultaneous localization and mapping (SLAM) has attracted high attention over the past few years. In this paper, a comprehensive survey of the state-of-the-art feature-based visual SLAM approaches is presented. The reviewed approaches are classified based on the visual features observed in the environment. Visual features can be seen at different levels; low-level features like points and edges, middle-level features like planes and blobs, and high-level features like semantically labeled objects. One of the most critical research gaps regarding visual SLAM approaches concluded from this study is the lack of generality. Some approaches exhibit a very high level of maturity, in terms of accuracy and efficiency. Yet, they are tailored to very specific environments, like feature-rich and static environments. When operating in different environments, such approaches experience severe degradation in performance. In addition, due to software and hardware limitations, guaranteeing a robust visual SLAM approach is extremely challenging. Although semantics have been heavily exploited in visual SLAM, understanding of the scene by incorporating relationships between features is not yet fully explored. A detailed discussion of such research challenges is provided throughout the paper.

show abstract

Section: Resultsmentioning

confidence: 99%

Feature-based visual simultaneous localization and mapping: a survey

Azzam

Taha²,

Huang

et al. 2020

SN Appl. Sci.

View full text Add to dashboard Cite

show abstract

“…For optimization-based SLAM, a poor initial guess of variables will lead to poor convergence performance. Rotation may be the cause of nonlinearity and has a strong impact on the divergence of estimation [205,206], thus the accumulated vehicle orientation error will cause the inconsistency of the SLAM problem. One solution to the linearization challenge is the Linear SLAM algorithm proposed in [205], which modifies the relative state vector and carries out "map joining".…”

Section: Issue Of Estimation Driftsmentioning

confidence: 99%

“…Rotation may be the cause of nonlinearity and has a strong impact on the divergence of estimation [205,206], thus the accumulated vehicle orientation error will cause the inconsistency of the SLAM problem. One solution to the linearization challenge is the Linear SLAM algorithm proposed in [205], which modifies the relative state vector and carries out "map joining". Sub-map joining, which involves solving a linear least squares problem and performing nonlinear coordinate transformations, does not require an initial guess or iteration.…”

Section: Issue Of Estimation Driftsmentioning

confidence: 99%

Simultaneous Localization and Mapping (SLAM) for Autonomous Driving: Concept and Analysis

et al. 2023

View full text Add to dashboard Cite

The Simultaneous Localization and Mapping (SLAM) technique has achieved astonishing progress over the last few decades and has generated considerable interest in the autonomous driving community. With its conceptual roots in navigation and mapping, SLAM outperforms some traditional positioning and localization techniques since it can support more reliable and robust localization, planning, and controlling to meet some key criteria for autonomous driving. In this study the authors first give an overview of the different SLAM implementation approaches and then discuss the applications of SLAM for autonomous driving with respect to different driving scenarios, vehicle system components and the characteristics of the SLAM approaches. The authors then discuss some challenging issues and current solutions when applying SLAM for autonomous driving. Some quantitative quality analysis means to evaluate the characteristics and performance of SLAM systems and to monitor the risk in SLAM estimation are reviewed. In addition, this study describes a real-world road test to demonstrate a multi-sensor-based modernized SLAM procedure for autonomous driving. The numerical results show that a high-precision 3D point cloud map can be generated by the SLAM procedure with the integration of Lidar and GNSS/INS. Online four–five cm accuracy localization solution can be achieved based on this pre-generated map and online Lidar scan matching with a tightly fused inertial system.

show abstract

“…As reviewed above, a key factor stymieing the performance enhancement in practice may refer to non-convexity in smoothing and locality in filtering; see for example [34] for an attempt of understanding the nonlinearity structure in one-step SLAM. With such a consideration, a natural question openly arises in [13] where it is stated: "How far is SLAM from a linear least squares problem?" The main purpose of this paper is to give an affirmative answer to the above question in the context of visual inertial SLAM without involving any linearization or approximation.…”

mentioning

confidence: 99%

An almost globally convergent observer for visual SLAM without persistent excitation

Jin

Wang

Shi

et al. 2021

2021 60th IEEE Conference on Decision and Control (CDC)

View full text Add to dashboard Cite

This paper introduces a new linear parameterization to the problem of visual inertial simultaneous localization and mapping (VI-SLAM)-without anyapproximation-for the case only using information from a single monocular camera and an inertial measurement unit. In this problem set, the system state evolves on the nonlinear manifold SE(3)×R 3n , on which we design dynamic extensions carefully to generate invariant foliations, such that the problem can be reformulated into online constant parameter identification, then interestingly with linear regression models obtained. It demonstrates that VI-SLAM can be translated into a linear least squares problem, in the deterministic sense, globally and exactly. Based on this observation, we propose a novel SLAM observer, following the recently established parameter estimation-based observer (PEBO) methodology. A notable merit is that the proposed observer enjoys almost global asymptotic stability, requiring neither persistency of excitation nor uniform complete observability, which, however, are widely adopted in most existing works with provable stability but can hardly be assured in many practical scenarios.

show abstract

How far is SLAM from a linear least squares problem?

Cited by 19 publications

References 14 publications

Feature-based visual simultaneous localization and mapping: a survey

Feature-based visual simultaneous localization and mapping: a survey

Simultaneous Localization and Mapping (SLAM) for Autonomous Driving: Concept and Analysis

An almost globally convergent observer for visual SLAM without persistent excitation

Contact Info

Product

Resources

About