Multiple Hypothesis Semantic Mapping for Robust Data Association

Bernreiter, Lukas; Gawel, Abel; Sommer, Hannes; Nieto, Juan; Siegwart, Roland; Cadena, César

doi:10.1109/lra.2019.2925756

Cited by 14 publications

(10 citation statements)

References 37 publications

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“…This is necessary both for introspection (i.e., to enable an autonomous agent to know what it doesn't know), and, by extension, for planning and active perception (to enable an autonomous agent to reason about how it could reduce its own uncertainty). The development of tractable estimation methods that can extract this richer information while scaling gracefully to high-dimensional problems is an important and fundamental open problem for future research in SLAM, although References (73,74,75) have proposed initial steps along these lines.…”

Section: Beyond Point Estimationmentioning

confidence: 99%

Advances in Inference and Representation for Simultaneous Localization and Mapping

Rosen¹,

Doherty²,

Espinoza³

et al. 2021

Preprint

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Simultaneous localization and mapping (SLAM) is the process of constructing a global model of an environment from local observations of it; this is a foundational capability for mobile robots, supporting such core functions as planning, navigation, and control. This article reviews recent progress in SLAM, focusing on advances in the expressive capacity of the environmental models used in SLAM systems (representation) and the performance of the algorithms used to estimate these models from data (inference). A prominent theme of recent SLAM research is the pursuit of environmental representations (including learned representations) that go beyond the classical attributes of geometry and appearance to model properties such as hierarchical organization, affordance, dynamics, and semantics; these advances equip autonomous agents with a more comprehensive understanding of the world, enabling more versatile and intelligent operation. A second major theme is a revitalized interest in the mathematical properties of the SLAM estimation problem itself (including its computational and informationtheoretic performance limits); this work has led to the development of novel classes of certifiable and robust inference methods that dramatically improve the reliability of SLAM systems in real-world operation. We survey these advances with an emphasis on their ramifications for achieving robust, long-duration autonomy, and conclude with a discussion of open challenges and a perspective on future research directions.

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Section: Beyond Point Estimationmentioning

confidence: 99%

Advances in Inference and Representation for Simultaneous Localization and Mapping

Rosen¹,

Doherty²,

Espinoza³

et al. 2021

Preprint

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show abstract

“…As a result, ambiguity in data association can be better resolved in later frames. MHDA has been popular in the 90's and successfully applied to MOT [26]- [28] and SLAM [29], [30]. However, at the time when MHDA was being actively developed, the topic of trajectory prediction was still in its infancy.…”

Section: Related Workmentioning

confidence: 99%

MTP: Multi-Hypothesis Tracking and Prediction for Reduced Error Propagation

Weng¹,

Ivanovic²,

Pavone³

2021

Preprint

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Recently, there has been tremendous progress in developing each individual module of the standard perceptionplanning robot autonomy pipeline, including detection, tracking, prediction of other agents' trajectories, and ego-agent trajectory planning. Nevertheless, there has been less attention given to the principled integration of these components, particularly in terms of the characterization and mitigation of cascading errors. This paper addresses the problem of cascading errors by focusing on the coupling between the tracking and prediction modules. First, by using state-of-the-art tracking and prediction tools, we conduct a comprehensive experimental evaluation of how severely errors stemming from tracking can impact prediction performance. On the KITTI and nuScenes datasets, we find that predictions consuming tracked trajectories as inputs (the typical case in practice) can experience a significant (even order of magnitude) drop in performance in comparison to the idealized setting where ground truth past trajectories are used as inputs. To address this issue, we propose a multi-hypothesis tracking and prediction framework. Rather than relying on a single set of tracking results for prediction, our framework simultaneously reasons about multiple sets of tracking results, thereby increasing the likelihood of including accurate tracking results as inputs to prediction. We show that this framework improves overall prediction performance over the standard single-hypothesis tracking-prediction pipeline by up to 34.2% on the nuScenes dataset, with even more significant improvements (up to ∼70%) when restricting the evaluation to challenging scenarios involving identity switches and fragments -all with a relatively minor computation overhead. Our project page is here: https://www.xinshuoweng.com/projects/MTP.

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“…Recently, SLAM techniques combining both geometric as well as semantic information have gained popularity and significant relevance [9]. It is now widely recognized that the incorporation of object-level information for accurate data associations and loop closures can increase the quality, robustness and interpretability of the solutions [10]- [12].…”

Section: Related Workmentioning

confidence: 99%

“…One of the latest publications on semantic SLAM [10] proposed to track different possible hypotheses for the data association, in a robust framework for the context of urban driving. Also very recently, Yang et al [12] proposed a unified SLAM framework including high level objects and planes based on monocular information.…”

Section: Related Workmentioning

confidence: 99%

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VPS-SLAM: Visual Planar Semantic SLAM for Aerial Robotic Systems

et al. 2020

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Indoor environments have abundant presence of high-level semantic information which can provide a better understanding of the environment for robots to improve the uncertainty in their pose estimate. Although semantic information has proved to be useful, there are several challenges faced by the research community to accurately perceive, extract and utilize such semantic information from the environment. In order to address these challenges, in this paper we present a lightweight and real-time visual semantic SLAM framework running on board aerial robotic platforms. This novel method combines low-level visual/visual-inertial odometry (VO/VIO) along with geometrical information corresponding to planar surfaces extracted from detected semantic objects. Extracting the planar surfaces from selected semantic objects provides enhanced robustness and makes it possible to precisely improve the metric estimates rapidly, simultaneously generalizing to several object instances irrespective of their shape and size. Our graph-based approach can integrate several state of the art VO/VIO algorithms along with the state of the art object detectors in order to estimate the complete 6DoF pose of the robot while simultaneously creating a sparse semantic map of the environment. No prior knowledge of the objects is required, which is a significant advantage over other works. We test our approach on a standard RGB-D dataset comparing its performance with the state of the art SLAM algorithms. We also perform several challenging indoor experiments validating our approach in presence of distinct environmental conditions and furthermore test it on board an aerial robot.

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Multiple Hypothesis Semantic Mapping for Robust Data Association

Cited by 14 publications

References 37 publications

Advances in Inference and Representation for Simultaneous Localization and Mapping

Advances in Inference and Representation for Simultaneous Localization and Mapping

MTP: Multi-Hypothesis Tracking and Prediction for Reduced Error Propagation

VPS-SLAM: Visual Planar Semantic SLAM for Aerial Robotic Systems

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