Real-Time Egocentric Navigation Using 3D Sensing

Smith, Justin S.; Feng, Shiyu; Lyu, Fanzhe; Vela, Patricio A.

doi:10.1007/978-3-030-22587-2_14

Cited by 11 publications

(15 citation statements)

References 78 publications

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“…The controller gains have been empirically tuned to give good performance for the perfect odometry case, and have been extensively used in prior work [1], [2], [33]. 4) Results and Analysis: Tables I to IV quantify the outcomes of all methods tested.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…In the time-varying trajectory tracking case, we assume that a trajectory reference h W C (t) exists along with a control signal u * (t) satisfying (2). It would typically be derived from a robot trajectory reference g W R (t) and control signal u * (t) satisfying (1).…”

Section: ) Trajectory and Control Signalsmentioning

confidence: 99%

“…In essence, the trajectory tracking problem is shifted from feedback in pose space to feedback in perception space. Perception space approaches have several favorable properties when used for navigation [1], [2]. Shifting the representation from being world-centric to being viewer-centric reduces computational demands and improves run-time properties.…”

Section: Introductionmentioning

confidence: 99%

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Trajectory Servoing: Image-Based Trajectory Tracking Using SLAM

Feng,

Wu,

Zhao

et al. 2021

Preprint

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This paper describes an image based visual servoing (IBVS) system for a nonholonomic robot to achieve good trajectory following without real-time robot pose information and without a known visual map of the environment. We call it trajectory servoing. The critical component is a featurebased, indirect SLAM method to provide a pool of available features with estimated depth, so that they may be propagated forward in time to generate image feature trajectories for visual servoing. Short and long distance experiments show the benefits of trajectory servoing for navigating unknown areas without absolute positioning. Trajectory servoing is shown to be more accurate than pose-based feedback when both rely on the same underlying SLAM system.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: ) Trajectory and Control Signalsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Trajectory Servoing: Image-Based Trajectory Tracking Using SLAM

Feng,

Wu,

Zhao

et al. 2021

Preprint

Self Cite

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“…A non-trivial performance gap emerges between idealized and actual models, the latter of which occupy space, have dynamics, can be nonholonomic, and have incomplete collision space measurements (from a limited field of view) [2], [3] . A variety of other planning schemes have been derived and refined to address the performance gap [4], [5]. In the context of long distance navigation, no single strategy provides the best outcome.…”

Section: Introductionmentioning

confidence: 99%

“…In the context of long distance navigation, no single strategy provides the best outcome. Hierarchical strategies permit multiple approaches that operate at different temporal and spatial scales [4], [6], [7]. More importantly, they consider the role of visual sensors in providing up-todate information regarding navigable space that complements existing and potentially stale free space maps.…”

Section: Introductionmentioning

confidence: 99%

Potential Gap: A Gap-Informed Reactive Policy for Safe Hierarchical Navigation

Feng

Vela

2021

IEEE Robot. Autom. Lett.

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This paper considers the integration of gap-based local navigation methods with artificial potential field (APF) methods to derive a local planning module for hierarchical navigation systems that has provable collision-free properties. Given that APF theory applies to idealized robot models, the provable properties are lost when applied to more realistic models. We describe a set of algorithm modifications that correct for these errors and enhance robustness to non-ideal models. Central to the construction of the local planner is the use of sensory-derived local free-space models that detect gaps and use them for the synthesis of the APF. Modifications are given for a nonholonomic robot model. Integration of the local planner, called potential gap, into a hierarchical navigation system provides the local goals and trajectories needed for collision-free navigation through unknown environments. Monte Carlo experiments in benchmark worlds confirm the asserted safety and robustness properties by testing under various robot models.

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Safe Hierarchical Navigation in Crowded Dynamic Uncertain Environments

Chen

Feng

Zhao

et al. 2022

2022 IEEE 61st Conference on Decision and Control (CDC)

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This paper describes a hierarchical solution consisting of a multi-phase planner and a low-level safe controller to jointly solve the safe navigation problem in crowded, dynamic, and uncertain environments. The planner employs dynamic gap analysis and trajectory optimization to achieve collision avoidance with respect to the predicted trajectories of dynamic agents within the sensing and planning horizon and with robustness to agent uncertainty. To address uncertainty over the planning horizon and real-time safety, a fast reactive safe set algorithm (SSA) is adopted, which monitors and modifies the unsafe control during trajectory tracking. Compared to other existing methods, our approach offers theoretical guarantees of safety and achieves collision-free navigation with higher probability in uncertain environments, as demonstrated in scenarios with 20 and 50 dynamic agents. Project website: https://hychen-naza.github.io/projects/HDAGap/.

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Real-Time Egocentric Navigation Using 3D Sensing

Cited by 11 publications

References 78 publications

Trajectory Servoing: Image-Based Trajectory Tracking Using SLAM

Trajectory Servoing: Image-Based Trajectory Tracking Using SLAM

Potential Gap: A Gap-Informed Reactive Policy for Safe Hierarchical Navigation

Safe Hierarchical Navigation in Crowded Dynamic Uncertain Environments

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