OrcVIO: Object residual constrained Visual-Inertial Odometry

Shan, Mo; Feng, Qiaojun; Atanasov, Nikolay

doi:10.1109/iros45743.2020.9341660

Cited by 28 publications

(14 citation statements)

References 29 publications

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“…Whereas early work (Bao and Savarese, 2011; Brostow et al, 2008) focused on offline processing, recent years have seen a surge of interest towards real-time metric–semantic mapping, triggered by pioneering works such as SLAM++ (Salas-Moreno et al, 2013). Object-based approaches compute an object map and include SLAM++ (Salas-Moreno et al, 2013), XIVO (Dong et al, 2017), OrcVIO (Shan et al, 2019), QuadricSLAM (Nicholson et al, 2018), and Bowman et al (2017). For most robotics applications, an object-based map does not provide enough resolution for navigation and obstacle avoidance.…”

Section: Related Workmentioning

confidence: 99%

Kimera: From SLAM to spatial perception with 3D dynamic scene graphs

Rosinol

Violette

Abate

et al. 2021

The International Journal of Robotics Research

141

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Humans are able to form a complex mental model of the environment they move in. This mental model captures geometric and semantic aspects of the scene, describes the environment at multiple levels of abstractions (e.g., objects, rooms, buildings), includes static and dynamic entities and their relations (e.g., a person is in a room at a given time). In contrast, current robots’ internal representations still provide a partial and fragmented understanding of the environment, either in the form of a sparse or dense set of geometric primitives (e.g., points, lines, planes, and voxels), or as a collection of objects. This article attempts to reduce the gap between robot and human perception by introducing a novel representation, a 3D dynamic scene graph (DSG), that seamlessly captures metric and semantic aspects of a dynamic environment. A DSG is a layered graph where nodes represent spatial concepts at different levels of abstraction, and edges represent spatiotemporal relations among nodes. Our second contribution is Kimera, the first fully automatic method to build a DSG from visual–inertial data. Kimera includes accurate algorithms for visual–inertial simultaneous localization and mapping (SLAM), metric–semantic 3D reconstruction, object localization, human pose and shape estimation, and scene parsing. Our third contribution is a comprehensive evaluation of Kimera in real-life datasets and photo-realistic simulations, including a newly released dataset, uHumans2, which simulates a collection of crowded indoor and outdoor scenes. Our evaluation shows that Kimera achieves competitive performance in visual–inertial SLAM, estimates an accurate 3D metric–semantic mesh model in real-time, and builds a DSG of a complex indoor environment with tens of objects and humans in minutes. Our final contribution is to showcase how to use a DSG for real-time hierarchical semantic path-planning. The core modules in Kimera have been released open source.

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Section: Related Workmentioning

confidence: 99%

Kimera: From SLAM to spatial perception with 3D dynamic scene graphs

Rosinol

Violette

Abate

et al. 2021

The International Journal of Robotics Research

141

View full text Add to dashboard Cite

show abstract

“…Multi-stage methods form another major paradigm for category-level perception. Such approaches first recover the position of semantic keypoints [56] in the images with neural networks, and then recover the 3D pose of the object by solving a geometric optimization problem [31,53,56,57,64]. In some works, a canonical coordinate space is predicted by a network instead of relying on geometric reasoning [14,22,41,78].…”

Section: Related Workmentioning

confidence: 99%

Optimal Pose and Shape Estimation for Category-level 3D Object Perception

Shi

Yang

Carlone

2021

Robotics: Science and Systems XVII

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We consider a category-level perception problem, where one is given 3D sensor data picturing an object of a given category (e.g., a car), and has to reconstruct the pose and shape of the object despite intra-class variability (i.e., different car models have different shapes). We consider an active shape model, where -for an object category-we are given a library of potential CAD models describing objects in that category, and we adopt a standard formulation where pose and shape estimation are formulated as a non-convex optimization. Our first contribution is to provide the first certifiably optimal solver for pose and shape estimation. In particular, we show that rotation estimation can be decoupled from the estimation of the object translation and shape, and we demonstrate that (i) the optimal object rotation can be computed via a tight (small-size) semidefinite relaxation, and (ii) the translation and shape parameters can be computed in closed-form given the rotation. Our second contribution is to add an outlier rejection layer to our solver, hence making it robust to a large number of misdetections. Towards this goal, we wrap our optimal solver in a robust estimation scheme based on graduated non-convexity. To further enhance robustness to outliers, we also develop the first graph-theoretic formulation to prune outliers in category-level perception, which removes outliers via convex hull and maximum clique computations; the resulting approach is robust to 70 − 90% outliers. Our third contribution is an extensive experimental evaluation. Besides providing an ablation study on a simulated dataset and on the PASCAL3D+ dataset, we combine our solver with a deep-learned keypoint detector, and show that the resulting approach improves over the state of the art in vehicle pose estimation in the ApolloScape datasets.

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“…While early work [7,14] focused on offline processing, recent years have seen a surge of interest towards real-time metricsemantic mapping, triggered by pioneering works such as SLAM++ [96]. Object-based approaches compute an object map and include SLAM++ [96], XIVO [21], OrcVIO [98], QuadricSLAM [74], and [12]. For most robotics applications, an object-based map does not provide enough resolution for navigation and obstacle avoidance.…”

Section: Introductionmentioning

confidence: 99%