multi-sensor fusion for target tracking · multi-sensor fusion for target tracking ... •critical...
TRANSCRIPT
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
Multi-sensor Fusion for Target Tracking
J. Ramiro Martínez-de Dios
Robotics, Computer Vision Group
University of Seville, Spain
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
• Localization and tracking in GNSS-denied environments are open R&D topics
• Critical for many applications in cinematography
Issues:
• Unstructured, complex and dynamic environments: sensor fusion
• Real-time problem: efficient use of resources
• On-board processing
• Robustness: decentralization
• Changing conditions: responsiveness, dynamic adaptation
Motivation and introduction
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
• Introduction
• The multi-sensor tracking problem
• Tools for multi-sensor tracking
• An example: fusion of camera and RSSI measurements
• Active tracking
• Conclusions
Outline
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
• Estimate target location and velocities
• General case: 6D (position and orientation) in location and velocities
• The tracking problems has been largely analyzed in the literature
• Data association is still not a solved problem: problem-based solutions
• This presentation focusses on multi-sensor
fusion for tracking
The Multi-sensor Tracking Problem
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
• One only sensor cannot capture tracking information in all conditions
• Unexpected issues event:
• Loss of GNSS signal
• Occlusions
• Shadows and lighting conditions
• Scenario changes
• Sensor or drone failure
• Communication failures
• Scenario with many uncertainty sources
Multi-sensor Fusion in Complex Scenarios
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
Multi-sensor Fusion in Complex Scenarios
Accurate localization in GNSS-denied environments
Sensors: ◦ Velodyne HDL-32E
◦ ZED stereo camera
◦ ToF range sensors
◦ UWB ToF sensors
◦ IMU
◦ Laser altimeter
◦ RTK D-GPS
Processing:
◦ Intel NUC
◦ Jetson TX2
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
Multi-sensor Fusion in Complex Scenarios
Pros Cons
Camera Accurate with short distances with the objects
Sensitive to lighting conditions
2D/3D LIDAR Accurate at moderate distances Requires feature-rich scenarios
Range-sensors (radio) - Naturally solves data association problem - Available at large distances
- Requires deploying sensors in the environment
- Radio interference with metallic objects
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
RTK GPS VS Multi-sensor Localization
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
J. R. Martínez-de Dios Email: [email protected]
Multi-sensor Localization
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
• USE, CATEC, AIRBUS D&S
• One of the two Finalists of EUROC-Challenge 3,
out of more than 35 teams.
• Award “Best Dron-based Solution”, EU Parliament, January 2017
• Drones as co-workers in factories
• Drone indoor auton. navigation
• Massive use of sensor-fusion
techniques
• High robustness
ARCOW: Aerial Robot CO-Worker
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
ARCOW: Aerial Robot CO-Worker
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
ARCOW: Aerial Robot CO-Worker
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
• Introduction
• The multi-sensor tracking problem
• Tools for multi-sensor tracking
• An example: fusion of camera and RSSI measurements
• Active tracking
• Conclusions
Outline
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
Recursive Bayesian Filters
• Strong mathematical foundation
• Explicit consideration of uncertainty in models and sensors Good performance in presence of noise
in sensors and models
• Very high flexibility: higher than traditional data fusion methods
• Allows modeling realistic systems: observations and systems under uncertainty
• Flexible approach: can be combined with other modules such as dynamic model-learning or uncertainty-
based supervisors
• It enables reasoning in terms of INFORMATION enabling combination with Information-based methods
& tools; e.g. POMDPs
Tools for Multi-sensor Tracking
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
• Optimal performance in presence of noise in sensors and models
• Very high flexibility: higher than traditional data fusion methods
• Allows modeling realistic systems: observations and systems under uncertainty
Probabilistic Bayesian Filters: RBFs
Actions
Disturbances
Estimator
Estimation
State
System Sensors
Noise
Sensor model
System model
Observations
Update
Prediction
measurements
xk+1|k
xk|k
zk
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
Kalman Filter:
• Use parametric models for the system and observations:
(mean vector) (covariance matrix)
• Assumes Gaussian noise (observation and model)
• Assumes Linear models
• Extension to non-linear models: Extended Kalman Filter (EKF)
Information Filter:
• Dual to the KF. Uses the canonical representation:
• Uses same assumptions as KF
• KF and IF have similar burden complexity
• KF are efficient in the Prediction Step
• IF are efficient in the Update Step. They can be decentralized
Scales well with the number of measurements
Probabilistic Bayesian Filters: RBFs
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
Particle Filter:
• Non-parametric representation: cloud of particles that represent the p.d.f. of the vector state
• Pros: no constrained in noise or system representations
• Pros: allows multi-hypothesis cases
• Cons: High computational burden (>100 particles)
Probabilistic Bayesian Filters: RBFs
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
Information Filters for Multi-sensor Tracking
Kalman Filter Information Filter Particle Filter
Decentralization Complex Natural Complex
Computational efficiency + +++
Flexibility, adaptability + +++
Scalability + +++
Numerical Stability +++
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
Decentralized Information Filter for Multi-sensor Tracking
non-head head
Notation: “Probabilistic Robotics”, Thrun et al.
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
• Distributed Extended Information Filter (EIF):
• State: object location and velocities
• Measurements: center on image plane (pin-hole nonlinear model)
• Prediction model:
Object follows a locally rectilinear trajectory
Decentralized Information Filter for Multi-sensor Tracking
A de San Bernabe, J.R. Martinez-de Dios, A Ollero, “Efficient cluster-based tracking mechanisms for camera-based wireless sensor networks”, IEEE Transactions on Mobile Computing 14 (9), 1820-1832
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
Drones perceiving the same object organize autonomously in a cluster
Advantages of cluster-based tracking: Scalable and robust
• Local processing of information: avoids transmission of normally
heavy traffic
• Tracking of several objects simultaneously, each with its cluster
The cluster head (CH) is responsible for:
• Collecting and fusing measurements from all the cluster nodes
• Managing inclusion/exclusion from the cluster
• Managing changing/rotation of cluster heads
Other Tracking Functionalities
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
• Introduction
• The multi-sensor tracking problem
• Tools for multi-sensor tracking
• An example: fusion of camera and RSSI measurements
• Active tracking
• Conclusions
Outline
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
Mechanisms:
A distributed EIF fuse the measurements
An entropy-based active sensing method for inclusion and exclusion of camera nodes in the cluster
A method that calibrates RSSI using camera measurements
Fusion of Cameras and RSSI for Tracking
A de San Bernabé, JR Martinez-de Dios, A Ollero, “Efficient integration of RSSI for tracking using Wireless Camera Networks” Information Fusion 36, 296-312
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
• Integrates available RSSI and camera measurements
Uses RSSI-range models
Fusion of Cameras and RSSI for Tracking
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
Motivation: In static scenarios RSSI has low variability
RSSI-range Training using Camera Measurements
Approach: use estimations of the object location to train in real-time RSSI-range models
The computed RSSI-model will be valid locally around the current target location
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
Estimate the model: linear for simplicity (it is only local)
Estimate the uncertainty of the trained model:
𝑅𝑆𝑆𝐼𝑖,𝑡 = 𝑎𝑑𝑖,𝑡 + 𝑏 𝑎𝑖 = 𝑅𝑆𝑆𝐼𝑖,𝑡𝑑𝑖,𝑡 − 𝑅𝑆𝑆𝐼𝑖 𝑑𝑖,𝑡𝑡𝑡
𝑑𝑖,𝑡2− 𝑑 𝑖 𝑑𝑖,𝑡𝑡𝑡
,
𝑏𝑖 = 𝑅𝑆𝑆𝐼𝑖 − 𝑎𝑑 𝑖 ,
RSSI-range Training using Camera Measurements
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
Experiments: Integrated Testbed
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
• Experimental results:
• Settings:
• 31 camera nodes
• One Pioneer 3-AT robot
Experiments: Integrated Testbed
3 Methods
1. Decentralized EIF with no mechanisms
2. 1 with node activation/deactivation
3. 2 with RSSI-range training
Proposed scheme with sensor activation/deactivation
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
• Introduction
• The multi-sensor tracking problem
• Tools for multi-sensor tracking
• An example: fusion of camera and RSSI measurements
• Active tracking
• Conclusions
Outline
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
• The CH dynamically activates/deactivates nodes balancing the usefulness of the measurements and
their costs
Active Perception for Reducing Tracking Uncertainty
Action 1
Action 2
A
B
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
• Balance of the usefulness of the measurements and their costs
• Greedy approach: at each time selects the action that maximizes the difference between reward and
cost
• Cost: energy spent to gather a new measurement
• Reward: the expected information gain from executing the action. Assuming Gaussian distribution, it can
be computed from the information matrices of the predicted and the prior states:
Active Perception for Reducing Tracking Uncertainty
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
Active Perception for Reducing Tracking Uncertainty
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
• Introduction
• The multi-sensor tracking problem
• Tools for multi-sensor tracking
• An example: fusion of camera and RSSI measurements
• Active tracking
• Conclusions
Outline
33
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
• In general using only one sensor cannot provide sufficient information for accurate
tracking in complex, dynamic scenarios
• Probabilistic estimation tools are very interesting tools:
• Can explicitly consider uncertainty
• Can naturally fuse measurements
• Can be complemented with supervisors and other modules
• Can be used to reason on information gain enabling active perception schemes
Conclusions
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This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 731667 (MULTIDRONE)
Multi-sensor Fusion for Target Tracking
J. Ramiro Martínez-de Dios Univ. de Sevilla
Robotics, Computer Vision Group
University of Seville, Spain