Globally Optimal Event-Based Divergence Estimation for Ventral Landing

McLeod, Sofia; Meoni, Gabriele; Izzo, Dario; Mergy, Anne; Liu, Daqi; Latif, Yasir; Reid, Ian; Chin, Tat-Jun

doi:10.1007/978-3-031-25056-9_1

Cited by 7 publications

(14 citation statements)

References 47 publications

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“…The first work on eventbased TTC using a single event camera relied solely on the estimation of visual motion flows [10], whereby the motion flows were computed by fitting a local plane to the time surface [4]. Other works that followed were geared towards two main use cases, namely obstacle avoidance [12,29,39] and ventral landing [28,36,43]. Event-based obstacle avoidance methods are built to be fast reacting and, although they come from either bio-inspired [29,39] or mathematically grounded principles [12], they typically rely on empirically-validated heuristics to speed-up computations.…”

Section: Related Workmentioning

confidence: 99%

“…VL Dataset [28] 2 . It consists of 7 real event sequences observing planar prints of landing surfaces and 1 real event sequence observing the 3D print of a landing surface.…”

Section: Datasets and Metricsmentioning

confidence: 99%

“…Due to the distinct visual sensing paradigm, however, new methods are necessary to fully exploit the potential of event cameras [13,23]. PF [36] CT [43] SOFAS [45] ECMD [28] E-RAFT [17] Proposed Runtime per event (microsec) AREE (%)…”

Section: Introductionmentioning

confidence: 99%

“…Runtime vs. accuracy comparison for TTC estimation methods. Average results on the Ventral Landing benchmark [28].…”

Section: Introductionmentioning

confidence: 99%

“…Event cameras have been used to address the problem of fast TTC estimation, which comes up often in the visionbased obstacle avoidance [10,12,29,39] and ventral landing [28,36] literature. The TTC is the time that would elapse before a camera reaches an obstacle/surface, assuming the current relative motion between them remains constant [10].…”

Section: Introductionmentioning

confidence: 99%

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Time-to-Contact Map by Joint Estimation of Up-to-Scale Inverse Depth and Global Motion using a Single Event Camera

Nunes,

Perrinet,

Ieng

2023

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

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Event cameras asynchronously report brightness changes with a temporal resolution in the order of microseconds, which makes them inherently suitable to address problems that involve rapid motion perception. In this paper, we address the problem of time-to-contact (TTC) estimation using a single event camera. This problem is typically addressed by estimating a single global TTC measure, which explicitly assumes that the surface/obstacle is planar and fronto-parallel. We relax this assumption by proposing an incremental event-based method to estimate the TTC that jointly estimates the (up-to scale) inverse depth and global motion using a single event camera. The proposed method is reliable and fast while asynchronously maintaining a TTC map (TTCM), which provides perpixel TTC estimates. As a side product, the proposed method can also estimate per-event optical flow. We achieve state-of-the-art performances on TTC estimation in terms of accuracy and runtime per event while achieving competitive performance on optical flow estimation.

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Section: Related Workmentioning

confidence: 99%

“…VL Dataset [28] 2 . It consists of 7 real event sequences observing planar prints of landing surfaces and 1 real event sequence observing the 3D print of a landing surface.…”

Section: Datasets and Metricsmentioning

confidence: 99%