Shibaura Institute of Technology

Advanced Driver Assistance System Lab

Chapter 11 – Localization and Map Making –

Advising teacher Porf.Toshio Ito

BQ12034 Atsushi Kutsuwa

Shibaura Institute of Technology

モータの制御方法について

1. Overview

Where am I ?Where have I been ?

Generally….・Location specification・Map making→A close relation exists for

correct map making.

answer

questions of navigation

Shibaura Institute of Technology

モータの制御方法について

2. Sonar sensor model

It's said that a sensor model needs to renew uncertainty. This sensor model is considered in the point that proved data collects right of the result.

The frequency of right of the data

Trust and model formation

A Polaroid sonar is used at this chapter.

Shibaura Institute of Technology

モータの制御方法について

2. Sonar sensor model

A Polaroid sonar converged on a model like a figure in the robotics field.

The view is prescribed by angle β of the half cone with biggest area R where detection is possible. It‘s possible to share the view with three of territory by a grid.

I. Occupation ⇒ Hill

II. Empty ⇒Valley

III. The state is unclear.⇒ Plane

Shibaura Institute of Technology

モータの制御方法について

3. Bayes regulation

The way used most changes the sensor measure and combines with Bayes's regulation to collect evidence.

With Bayes's formula made with Moraz Beck of CMU ……An advantage on the calculation and the precondition exist.

⇒ A sensor model produces conditional probability P (s|H) by Bayes's approach. After that I change to P (H|s).

Sensor different in 2 of simultaneous sensing Something to come near at time when 2 are different

It's possible to twist two probabilities in Bayes regulation.

Shibaura Institute of Technology

モータの制御方法について

3. Bayes regulation

Conditional probability

Without relations, both can show what there is at the detected place by a probability of vanity or occupation.

It's calculated every two elements by an occupation grid.

Conditional probability by P(H|s)

Shibaura Institute of Technology

モータの制御方法について

4. Evidence theory of Dempster and Scheffer

Different evidence theory is evidence theory of Dempster and Scheffer. This invents a result similar to Bayes science of statistics.

Bayes science of statistics ：⇒I depend on a result of the probability mass function.

BEDENPUSUTA evidence theory ：⇒Probability evidence of the faith function

The probability faith function is called the Sheaffer function. It's combined with the combination Dempster formula.

⇒A result is akin to Bayes formula, but the method is different.

The Dempster formula indicates the disagreement condition of the various observations.

Shibaura Institute of Technology

モータの制御方法について

5.HIMM

HIMM is an abbreviation of The Histogrammic in Motion Mapping. A specific element in the occupation grid gives occupation or the approach which decides whether it's empty to this algorithm.⇒ Born for improvement of obstacle avoidance by 0.8m/s.

Bayesian model is used up to now. ． Fast algorithm by the huge calculated amount

It won the victory in the play from which I move a road. ．⇒ An occupation grid fell behind with the standard by many play robots with HIMM.

Shibaura Institute of Technology

モータの制御方法について

6. Consideration of a method

Bayes technique and the Dempster Sheaffer technique

⇒The measure like the stereo and the laser can fuse

easily so that a sensor model may exist.

HIMM

⇒Restriction to a sonar exists. But there is an advantage on the calculation.

Shibaura Institute of Technology

モータの制御方法について

6. Consideration of a method

When the hallway a robot penetrates is indicated, it's predominant a little compared with Bayes and Dempster grid. But actually, Bayes and DempsterSheaffer have a few parameters which fit a change in the environment.

a.)Bayesian b.)Dempster-Shafer c.)HIMM

Shibaura Institute of Technology

モータの制御方法について

7. Location presumption

Location presumption uses the feature quantity from the sensor data directly. Location presumption by this feature quantity is akin to a way of thinking of topology Cal. in the point that it's possible to judge from several viewpoints.

A making method on the present map is dependent on location presumption of the feature quantity big, and many methods are using several shapes of the location presumption and the map fitness.

Shibaura Institute of Technology

モータの制御方法について

7. Location presumption

Map fitness is complicated by an occupation grid.

⇒n is more excellent than n-1.

It's akin to a way based on the feature of topo logical map making because relationship of a gateway is clear.

Problem

Possibility that an intersection with a hallway is interpreted by mistake as a door

Shibaura Institute of Technology

モータの制御方法について

7. Location presumption

Sheaffer compares the technique based on the feature with an image.

The technique by the image ⇒problem to reduce dependence in the environment.

The technique by the feature ⇒There are few amounts of data. To do algorithm during

presumption, it's fast.

- Important point -It isn't treated with location presumption targeted for the one which moves.⇒The measure of the past and present, identical processing is make fry.

Shibaura Institute of Technology

モータの制御方法について

8. Traceroute

Problem however efficiently whether an unknown place is covered.

Voronoi method

The short-term endurance of the peculiar judge is used.→Execution is easy, the unknown territory which is two or more on the other hand, it takes time for setting.

Frontier methodIt moves around at random and I search. → Huge time is spent in many cases, but all

territory can be covered.

Shibaura Institute of Technology

モータの制御方法について

9. Summary

Map making changes information around the robot irrespective of the location of the robot.

The general data structure on a measurement map is an occupation grid.

Bayes approach

Dempster Sheaffer way

HIMM way

The high precision

The calculated amount is least.

Shibaura Institute of Technology

モータの制御方法について

9. Summary

An occupation grid as a virtual sensor is used for improvement of a sensor fusion.

Ex.) Location presumption is necessary for global map making by a fixed coordinate.

The same frequency as reacting behavior bears calculation cost.

It's on a correct map so that there are a lot of estimated frequencies.

Shibaura Institute of Technology

モータの制御方法について

9. Summary

The raw sensor data is incomplete.⇒Unstableness of location presumption and map making

Most process technology does" location presumption" at the same time with map making.

Those, on a direct map, observational data, I fit in.

Anything based on the feature doesn't face to the map making. But it's suitable for topo logical map making. Later, it'll be more suitable for the present location on the map than measurement of a sensor.Two problems⇒ Detection in a doubtful place and the place which can't be classified

it's run, distance measurement way

Object whole The feature

Shibaura Institute of Technology

モータの制御方法について

9. Summary

Location presumption by a topo logical map⇒I do by checking a new gateway with the present gateway on the map.

The map making is weak in the environment which changes.

New practice ： Frontier base and GVGFrontier ⇒I focus on the territory one on the

occupation grid detected and rank an area.GVG⇒The geometric special quality is used more

than Voronoi diagram.