Simultaneous Localization and Mapping for Event-Based Vision Systems

Weikersdorfer, David; Hoffmann, Raoul; Conradt, Jörg

doi:10.1007/978-3-642-39402-7_14

Cited by 101 publications

(90 citation statements)

References 9 publications

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“…Localization using a DVS on a ground robot was first presented in [14] and later extended to Simultaneous Localization And Mapping (SLAM) in [15]. However, the system was limited to planar motion and a 2D map.…”

Section: B Related Workmentioning

confidence: 99%

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Event-based, 6-DOF pose tracking for high-speed maneuvers

Mueggler

Huber

Scaramuzza

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

201

167

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Abstract-In the last few years, we have witnessed impressive demonstrations of aggressive flights and acrobatics using quadrotors. However, those robots are actually blind. They do not see by themselves, but through the "eyes" of an external motion capture system. Flight maneuvers using onboard sensors are still slow compared to those attainable with motion capture systems. At the current state, the agility of a robot is limited by the latency of its perception pipeline. To obtain more agile robots, we need to use faster sensors. In this paper, we present the first onboard perception system for 6-DOF localization during high-speed maneuvers using a Dynamic Vision Sensor (DVS). Unlike a standard CMOS camera, a DVS does not wastefully send full image frames at a fixed frame rate. Conversely, similar to the human eye, it only transmits pixel-level brightness changes at the time they occur with microsecond resolution, thus, offering the possibility to create a perception pipeline whose latency is negligible compared to the dynamics of the robot. We exploit these characteristics to estimate the pose of a quadrotor with respect to a known pattern during high-speed maneuvers, such as flips, with rotational speeds up to 1,200• /s. Additionally, we provide a versatile method to capture ground-truth data using a DVS.

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Section: B Related Workmentioning

confidence: 99%

“…We used a probabilistic framework that updates the pose likelihood relative to the previous CMOS frame by processing each event individually as soon as it arrives. As in [15], the experiments were performed at relatively low speeds (up to 30…”

Section: B Related Workmentioning

confidence: 99%

Event-based, 6-DOF pose tracking for high-speed maneuvers

Mueggler

Huber

Scaramuzza

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

201

167

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“…A particle-filter approach for robot self-localization using the DVS was introduced in [28] and later extended to SLAM in [29]. However, the system was limited to planar motions and 2-D maps.…”

Section: Related Work: Ego-motion Estimation With Event-based VImentioning

confidence: 99%

“…Eventbased adaptations of iterative closest points [24] and optical flow [5] have already been proposed. Recently, event-based visual odometry [9,17], tracking [28,23], and Simultaneous Localization And Mapping (SLAM) [29] algorithms have also been presented. The design goal of such algorithms is that each incoming event can asynchronously change the estimated state of the system, thus, preserving the event-based nature of the sensor and allowing the design of highly-reactive systems, such as pen balancing [10] or particle tracking in fluids [12].…”

Section: Introductionmentioning

confidence: 99%

Continuous-Time Trajectory Estimation for Event-based Vision Sensors

Mueggler¹,

Gallego²,

Scaramuzza³

2015

Robotics: Science and Systems XI

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Abstract-Event-based vision sensors, such as the Dynamic Vision Sensor (DVS), do not output a sequence of video frames like standard cameras, but a stream of asynchronous events. An event is triggered when a pixel detects a change of brightness in the scene. An event contains the location, sign, and precise timestamp of the change. The high dynamic range and temporal resolution of the DVS, which is in the order of micro-seconds, make this a very promising sensor for high-speed applications, such as robotics and wearable computing. However, due to the fundamentally different structure of the sensor's output, new algorithms that exploit the high temporal resolution and the asynchronous nature of the sensor are required. In this paper, we address ego-motion estimation for an event-based vision sensor using a continuous-time framework to directly integrate the information conveyed by the sensor. The DVS pose trajectory is approximated by a smooth curve in the space of rigid-body motions using cubic splines and it is optimized according to the observed events. We evaluate our method using datasets acquired from sensor-in-the-loop simulations and onboard a quadrotor performing flips. The results are compared to the ground truth, showing the good performance of the proposed technique.

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“…Additionally the resampling operation is computationally expensive when executed several thousand times per second. For these reasons an incremental model is chosen where the Markov assumption is released and the score of particles does not only depend on the current measurement but also on the recent past of measurements [12], [13]. For each new event e k particle scores are updated using an exponential decay model:…”

Section: Event-based 3d Slammentioning

confidence: 99%

Event-based 3D SLAM with a depth-augmented dynamic vision sensor

Weikersdorfer

Adrian

Cremers

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

Self Cite

117

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Abstract-We present the D-eDVS-a combined event-based 3D sensor -and a novel event-based full-3D simultaneous localization and mapping algorithm which works exclusively with the sparse stream of visual data provided by the D-eDVS. The D-eDVS is a combination of the established PrimeSense RGB-D sensor and a biologically inspired embedded dynamic vision sensor. Dynamic vision sensors only react to dynamic contrast changes and output data in form of a sparse stream of events which represent individual pixel locations. We demonstrate how an event-based dynamic vision sensor can be fused with a classic frame-based RGB-D sensor to produce a sparse stream of depth-augmented 3D points. The advantages of a sparse, event-based stream are a much smaller amount of generated data, thus more efficient resource usage, and a continuous representation of motion allowing lag-free tracking. Our eventbased SLAM algorithm is highly efficient and runs 20 times faster than realtime, provides localization updates at several hundred Hertz, and produces excellent results. We compare our method against ground truth from an external tracking system and two state-of-the-art algorithms on a new dataset which we release in combination with this paper.

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Simultaneous Localization and Mapping for Event-Based Vision Systems

Cited by 101 publications

References 9 publications

Event-based, 6-DOF pose tracking for high-speed maneuvers

Event-based, 6-DOF pose tracking for high-speed maneuvers

Continuous-Time Trajectory Estimation for Event-based Vision Sensors

Event-based 3D SLAM with a depth-augmented dynamic vision sensor

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