eVO: A realtime embedded stereo odometry for MAV applications

Sanfourche, Martial; Vittori, Vincent; Besnerais, Guy Le

doi:10.1109/iros.2013.6696651

Cited by 40 publications

(27 citation statements)

References 25 publications

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“…It was also featuring moving people and furniture. To perform localization and mapping, we have used an architecture made of the EVO algorithm (Sanfourche et al, 2013) and OctoMap (Hornung et al, 2013). Figure 12 represents part of the contest environment built during the contest.…”

Section: Resultsmentioning

confidence: 99%

AMPLE: an anytime planning and execution framework for dynamic and uncertain problems in robotics

et al. 2018

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Acting in robotics is driven by reactive and deliberative reasonings which take place in the competition between execution and planning processes. Properly balancing reactivity and deliberation is still an open question for harmonious execution of deliberative plans in complex robotic applications. We propose a flexible algorithmic framework to allow continuous real-time planning of complex tasks in parallel of their executions. Our framework, named AMPLE, is oriented towards robotic modular architectures in the sense that it turns planning algorithms into services that must be generic, reactive, and valuable. Services are optimized actions that are delivered at precise time points following requests from other modules that include states and dates at which actions are needed. To this end, our framework is divided in two concurrent processes: a planning thread which receives planning requests and delegates action selection to embedded planning softwares in compliance with the queue of internal requests, and an execution thread which orchestrates these planning requests as well as action execution and state monitoring. We show how the behavior of the execution thread can be parametrized to achieve various strategies which can differ, for instance, depending on the distribution of internal planning requests over possible future execution states in anticipation of the uncertain evolution of the system, or over different underlying planners to take several levels into account. We demonstrate the flexibility and the relevance of our framework on various robotic benchmarks and real experiments that involve complex planning problems of different natures which could not be properly tackled by existing dedicated planning approaches which rely on the standard plan-then-execute loop.

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Section: Resultsmentioning

confidence: 99%

AMPLE: an anytime planning and execution framework for dynamic and uncertain problems in robotics

et al. 2018

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“…However, enough spatial covering is needed to allow matching of features between key-frames. Following a procedure previously used for Simultaneous Localisation and Mapping (SLAM) [5], keyframes are chosen according to statistics of feature tracking over the sequence. This iterative algorithm proceeds as follows.…”

Section: A Refinement Of Trajectory Parameters By Bundle Adjustmentmentioning

confidence: 99%

Environment mapping & interpretation by drone

Sanfourche

Saux

Plyer

et al. 2015

2015 Joint Urban Remote Sensing Event (JURSE)

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In this paper we present the processing chain for geometric and semantic mapping of a drone environment that we developed for search-and-rescue purposes. A precise 3D modelling of the environment is computed using video and (if available) Lidar data captured during the drone flight. Then semantic mapping is performed by interactive learning on the model, thus allowing generic object detection. Finally, tracking of moving objects are performed in the video stream and localized in the 3D model, thus giving a global view of the situation. We assess our system on real data captured on various test locations.

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“…Dense stereo is computed for each stereo acquisition time and dense optical flow is computed between successive times: these costly lowlevel operations are discussed in the following. We use the "efficient Visual Odometry" (eVO) of [13] which can run at 20Hz on a single core of an embedded CPU: some details on this estimation process will be reviewed in Sec. III.…”

Section: System Descriptionmentioning

confidence: 99%

“…In our case, we choose the same odometry as the one used in [13], i.e. we minimize in a RANSAC procedure [21] the reprojection error…”

Section: B (R T ) Estimation Errormentioning

confidence: 99%

Real-time mobile object detection using stereo

Derome

Plyer

Sanfourche

et al. 2014

2014 13th International Conference on Control Automation Robotics &Amp; Vision (ICARCV)

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This paper considers passive vision for robotics and focuses on devising a real-time process for moving object detection using a stereo rig. As several previous works, our method relies on the use of dense stereo and of optical flow. Observing that the main computational load of existing methods is related to the estimation of the optical flow, we propose to use a fast algorithm based on Lucas-Kanade's paradigm. We derive a new uncertainty model which explicitly takes into account all errors originating from each estimation step of the process. In contrast with most previous works, we describe a rigorous expansion of the error related to vision based ego-motion estimation. Finally, we present a comparative study of performance on the KITTI dataset, which demonstrates the effectiveness of the proposed approach.

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eVO: A realtime embedded stereo odometry for MAV applications

Cited by 40 publications

References 25 publications

AMPLE: an anytime planning and execution framework for dynamic and uncertain problems in robotics

AMPLE: an anytime planning and execution framework for dynamic and uncertain problems in robotics

Environment mapping & interpretation by drone

Real-time mobile object detection using stereo

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eVO: A realtime embedded stereo odometry for MAV applications

Cited by 40 publications

References 25 publications

AMPLE: an anytime planning and execution framework for dynamic and uncertain problems in robotics

AMPLE: an anytime planning and execution framework for dynamic and uncertain problems in robotics

Environment mapping &amp; interpretation by drone

Real-time mobile object detection using stereo

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Product

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Environment mapping & interpretation by drone