3D localization - Revision history

S123950: /* Results to date */

2017-07-20T12:17:07Z

Results to date

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	==Results to date==		==Results to date==

	Previous results can be found in the ''Related papers/Previous DTU projects'' folder in the Github repository.		Previous results can be found in the ''Related papers/Previous DTU projects'' folder in the Github repository.

	==Further work==		==Further work==

S123950: /* Results to date */

2017-07-20T12:16:32Z

Results to date

← Older revision		Revision as of 14:16, 20 July 2017
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	==Results to date==		==Results to date==

	~~Results will be added during the next three weeks (June, 2016).~~ Previous results can be found in the ''Related papers/Previous DTU projects'' folder in the Github repository.		Previous results can be found in the ''Related papers/Previous DTU projects'' folder in the Github repository.

	==Further work==		==Further work==

S123950 at 12:16, 20 July 2017

2017-07-20T12:16:12Z

← Older revision		Revision as of 14:16, 20 July 2017
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	The last method of solving the optimization problem discussed here, will be the Extended Kalman filter. This way is, like the particle filter, a statistical filter capable of estimating a number of details about the system such as position, velocity and even accelerometer bias.		The last method of solving the optimization problem discussed here, will be the Extended Kalman filter. This way is, like the particle filter, a statistical filter capable of estimating a number of details about the system such as position, velocity and even accelerometer bias.

	As the Kalman filter is still not fully implemented, and thus not tested, this section still needs some information about the Kalman filter~~. This information will be added during the 3 weeks period of June, 2016~~.		As the Kalman filter is still not fully implemented, and thus not tested, this section still needs some information about the Kalman filter.

	===Sensor fusion===		===Sensor fusion===

S123950: /* Further work */

2016-06-20T18:32:05Z

Further work

← Older revision		Revision as of 20:32, 20 June 2016
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	* Peer-to-peer mesh ranging scheme		* Peer-to-peer mesh ranging scheme
	*; Allow all of the devices to range to all other devices.		*; Allow all of the devices to range to all other devices.
	* Sensor fusion in EKF or preferably UKF
	*; Implement a proper Kalman filter to fuse info from ranges, IMU and pressure data into a best-case state estimate.
	* Message push-through option		* Message push-through option
	*; A structured way of utilizing the large data bandwidth of the system, in order to allow devices to exchange communication that is not ranging or position related.		*; A structured way of utilizing the large data bandwidth of the system, in order to allow devices to exchange communication that is not ranging or position related.
	* Logging
	*; An easy and structured way to log data and set where to output which data at runtime (Debug, Error, Warning, Info). The implementation should furthermore have a log-level that can be set.
	* Output API		* Output API
	*; A proper API on how the host devices can acquire data from the tags, through I2C, USB, UART og SPI. Also allowing a data driven scheme with a physical interrupt pin.		*; A proper API on how the host devices can acquire data from the tags, through I2C, USB, UART og SPI. Also allowing a data driven scheme with a physical interrupt pin.

S123950 at 16:42, 2 June 2016

2016-06-02T16:42:46Z

← Older revision		Revision as of 18:42, 2 June 2016
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	[[File:System close.jpg\|thumb\|right\|600px\|Full system with 6 anchors and 3 tags.]]		[[File:System close.jpg\|thumb\|right\|600px\|Full system with 6 anchors and 3 tags.]]

	~~The developed~~ three dimensional localization system, is intended to be used as closed-environment system, meaning that it is intended to not require anything of the host carrying the tag. This means that the system is extremely versatile and can be used almost anywhere, as the host is not required to run any timing specific software. The system can be used in every scenario where it is wanted to obtain some sort of position or velocity information about something, whether it is for ground-truth information, control feedback, surveillance or the like. A few examples of such places could be:		This page gives an overview of a three dimensional localization system developed. The system can be used in every scenario where it is wanted to obtain some sort of position or velocity information about something, whether it is for ground-truth information, control feedback, surveillance or the like. A few examples of such places could be:

	* Robotic control position and velocity feedback, for autonomous usage		* Robotic control position and velocity feedback, for autonomous usage
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	The position and velocity estimates are available wherever they are needed, whether that is onboard the host as data extraction from the tag itself or at a specific anchor for data logging purposes. <br />		The position and velocity estimates are available wherever they are needed, whether that is onboard the host as data extraction from the tag itself or at a specific anchor for data logging purposes. <br />
	Furthermore the system features extremely rapid deployment, as the anchors will be able to find their own position in a matter of seconds from first power. This means that the system can be set-up and ready to be used in a matter of minutes, without loss of accuracy.		Furthermore the system features extremely rapid deployment, as the anchors will be able to find their own position in a matter of seconds from first power. This means that the system can be set-up and ready to be used in a matter of minutes, without loss of accuracy.

			The developed system, is intended to be used as closed-environment system, meaning that it is intended to not require anything of the host carrying the tag. This means that the system is extremely versatile and can be used almost anywhere, as the host is not required to run any timing specific software.

S123950: /* Extended Kalman filter */

2016-06-02T11:37:04Z

Extended Kalman filter

← Older revision		Revision as of 13:37, 2 June 2016
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	The last method of solving the optimization problem discussed here, will be the Extended Kalman filter. This way is, like the particle filter, a statistical filter capable of estimating a number of details about the system such as position, velocity and even accelerometer bias.		The last method of solving the optimization problem discussed here, will be the Extended Kalman filter. This way is, like the particle filter, a statistical filter capable of estimating a number of details about the system such as position, velocity and even accelerometer bias.

	~~TODO: Elaborate on~~ the ~~use~~ of ~~Kalman~~		As the Kalman filter is still not fully implemented, and thus not tested, this section still needs some information about the Kalman filter. This information will be added during the 3 weeks period of June, 2016.

	===Sensor fusion===		===Sensor fusion===

S123950 at 11:30, 2 June 2016

2016-06-02T11:30:46Z

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	* Intelligent transportation in regards to eg. smarter vehicles		* Intelligent transportation in regards to eg. smarter vehicles

	The position and velocity estimates are available wherever they are needed, whether that is onboard the host as data extraction from the tag itself or at a specific anchor for data logging purposes.		The position and velocity estimates are available wherever they are needed, whether that is onboard the host as data extraction from the tag itself or at a specific anchor for data logging purposes. <br />
			Furthermore the system features extremely rapid deployment, as the anchors will be able to find their own position in a matter of seconds from first power. This means that the system can be set-up and ready to be used in a matter of minutes, without loss of accuracy.

S123950 at 11:29, 2 June 2016

2016-06-02T11:29:19Z

← Older revision		Revision as of 13:29, 2 June 2016
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	* Intelligent transportation in regards to eg. smarter vehicles		* Intelligent transportation in regards to eg. smarter vehicles

			The position and velocity estimates are available wherever they are needed, whether that is onboard the host as data extraction from the tag itself or at a specific anchor for data logging purposes.

S123950 at 11:27, 2 June 2016

2016-06-02T11:27:34Z

← Older revision		Revision as of 13:27, 2 June 2016
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	[[File:System close.jpg\|thumb\|right\|600px\|Full system with 6 anchors and 3 tags.]]		[[File:System close.jpg\|thumb\|right\|600px\|Full system with 6 anchors and 3 tags.]]

	~~TODO: Short intro~~ to the system, the ~~need~~ for such ~~system~~, ~~possible applications and a short overview~~.		The developed three dimensional localization system, is intended to be used as closed-environment system, meaning that it is intended to not require anything of the host carrying the tag. This means that the system is extremely versatile and can be used almost anywhere, as the host is not required to run any timing specific software. The system can be used in every scenario where it is wanted to obtain some sort of position or velocity information about something, whether it is for ground-truth information, control feedback, surveillance or the like. A few examples of such places could be:

			* Robotic control position and velocity feedback, for autonomous usage
			* Ground-truth to test other approaches
			* Inventory tagging
			* Personnel location information
			* Intelligent transportation in regards to eg. smarter vehicles



	==Hardware==		==Hardware==

S123950: /* Hardware */

2016-06-02T11:09:00Z

Hardware

← Older revision		Revision as of 13:09, 2 June 2016
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	==Hardware==		==Hardware==
	The localization system at hand consists of at least ~~4 '''~~anchors~~'''~~ and ~~a '''~~tag~~'''~~. <br />		The localization system at hand consists of at least four anchors and one tag. <br />
	The hardware design considerations, descriptions and overviews can be found at the [[Localization Hardware]] page. <br />		The hardware design considerations, descriptions and overviews can be found at the [[Localization Hardware]] page. <br />
	Each device communicates and ranges with each other through an UWB ([https://en.wikipedia.org/wiki/Ultra-wideband Ultra-wideband]) radio.		Each device communicates and ranges with each other through an UWB ([https://en.wikipedia.org/wiki/Ultra-wideband Ultra-wideband]) radio.
	<br />		<br />
	The system can be used by placing the anchors at fixed known positions in the room, preferably in different heights, after which the system will measure the distance from each anchor to the tag. These distances can then be combined with the known position of the associated anchor, in order to estimate the absolute position of the tag in three dimensions. The position will be calculated locally on the tag, and can as such be used to eg. control a robot carrying the tag.		The system can be used by placing the anchors at fixed known positions in the room, preferably in different heights, after which the system will measure the distance from each anchor to the tag. These distances can then be combined with the known position of the associated anchor, in order to estimate the absolute position of the tag in three dimensions. The position will be calculated locally on the tag, and can as such be used to eg. control a robot carrying the tag.



	==Software download==		==Software download==