Probabilistic data association for semantic SLAM

By Sean L. Bowman, Nikolay A. Atanasov, Kostas Daniilidis, George Pappas. 2017

471 citations recorded by the source corpus.

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Citations in the corpus

Continuous Scene Representations for Embodied AI (2022, background): “There are object-based maps that encode objectlevel semantics and the relationships [3, 7, 42].”
A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers (2022, background): “Instead of geometric features, a sparse map can capture objects, described by a semantic category, pose, and shape [91], [92].”
Seeing the Un-Scene: Learning Amodal Semantic Maps for Room Navigation (2020, background): “Some works formulate active SLAM as Partially Observable Markov Decision Process and use either Bayesian Optimization [27] or Reinforcement Learning [23] to plan trajectories that lead to accurate maps.”
Global Pose Estimation with an Attention-Based Recurrent Network (2018, background): “Several such solvers achieve excellent performance, for example, g2o [25], TSAM [8], Ceres, iSAM [20], SLAM++ [38], and recently [2] for optimization with semantic data association.”
SuMa++: Efficient LiDAR-based Semantic SLAM (2019, methodology): “Motivated by the advances of deep learning and Convolutional Neural Networks (CNNs) for scene understanding, there have been many semantic SLAM techniques exploiting this information using cameras [5], [31], cameras + IMU data [4], stereo cameras [10], [15], [18], [33], [38], or RGB-D sensors [3], [”
Kimera: From SLAM to spatial perception with 3D dynamic scene graphs (2021, methodology): “, 2015), research in these two fields has traditionally proceeded in isolation, and there has been a recent and growing research at the intersection of these areas (Bao and Savarese, 2011; Bowman et al., 2017; Cadena et al., 2016; Davison, 2018; Grinvald et al., 2019; Hackel et al., 2017; Zheng et a”
Object Goal Navigation using Goal-Oriented Semantic Exploration (2020, background): “Some of the more relevant works incorporate semantics in the map using probabilistic graphical models [3] or using recent learning-based computer vision models [48, 29].”
CubeSLAM: Monocular 3-D Object SLAM (2018, background): “Uncertain data association of object SLAM is addressed in [26].”
3D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans (2020, background): “, [8, 12, 30, 67, 85, 93, 97]) mostly focuses on “flat” representations (object constellations, metric-semantic meshes or volumetric models) that are not hierarchical in nature.”
Combining Optimal Control and Learning for Visual Navigation in Novel Environments (2019, unknown): “To overcome these limitations, task planning approaches have been proposed that leverage object and region speciﬁc semantics during planning [4, 7, 9, 13, 33]; however, often hand-tuned heuristics are used to leverage these semantics in planning.”
Variational End-to-End Navigation and Localization (2018, background): “However, our notion of visual matching is much more high-level, more akin to semantic visual localization and SLAM [26], [27], where the semantic-level features are driving affordances.”
Open-vocabulary Queryable Scene Representations for Real World Planning (2022, background): “Semantic SLAM [9]–[11] is an augmentation over traditional SLAM, it assigns semantic features over geometric features provided by SLAM (points, lines, planes).”
Visual-Inertial Navigation: A Concise Review (2019, background): “Moreover, a few recent research efforts have attempted to endow VINS with semantic understanding of environments [167, 168, 169, 170], which is only sparsely explored but holds great potentials.”
Hydra: A Real-time Spatial Perception System for 3D Scene Graph Construction and Optimization (2022, unknown): “The literature has focused on both object-based maps [10, 14, 41, 42, 53, 56] and dense maps, including volumetric models [23, 38, 40], point clouds [6, 32, 61], and 3D meshes [48, 51].”