Original subtitles

In this lecture, we will consider correlation based matching strategies for

location a robot on a map given laser range data.

This map registration process provides a very precise complement

to odometry based localization.

First we should introduced the LIDAR depth sensor.

LIDAR stands for light detection and ranging and

it provides distance measurements.

Often engineered in a laser scanner to provide two dimensional data.

The laser scanner we will model in this lecture

takes depth measurements in polar coordinates,

where a continuous distance reading r is made at discrete angles theta.

Here, theta encompasses 270 degrees, not a full circle.

The laser scanner can only see 10 to 30 meters away.

In this range restriction, means that distance measurements

showing here is black dots, can only be found within the area in green.

Thus, due to the rays generating from a single point and the limited range,

the robot can only see the dotted lines and not the lines in brown.

Just as in the previous lecture, a two dimensional occupancy grid map will

be used in localization, where a light colored cell represents high

probability of an obstacle and a dark colored cell present a low probability.

The cells here are meant to replicate the laser skin

shown in the previous slide when the robot is approaching a corner in a hallway.

Because the robot lives in a finite grid world the grid must sometimes be expanded

as the robot can escape the boundaries.

In this case, the map representation should increase in size as the robot

turns and travels on the corridor shown in the top left of this map or

else information will be lost.

In addition to mapping the laser data discussed in week 3, we can

access map data and try to find the robot pose in the map given the laser data.

The complimentary stages of mapping and localization when performed together

are known as SLAM, simultaneous localization and

mapping, which is a major research topic in robotics.

In the localization problem we have two sets of information.

First, the occupancy grid map provides a grounds truth knowledge

of what the robot should expect to observe in the world.

Second, the set of lighter scan measurements

provides information on what the robot is observing at the current time.

The lighter scan measurements must be discretized

according to the map representation, as discussed in week three,

in order to be compared to the information from the occupancy map.

With these two pieces of information the goal is to find the best robot pose

on the map that explains the measured observations.

Searching over all possible poses of the robot can be difficult.

But based on the odometry information discussed in the last lecture,

we have some tricks to make the search easier.

We can constrain the search to a limited number of poses

based on odometry information.

Because we track the robot over time, we have the last known position of the robot

and odometry information on how far the robot most likely moved.

Thus, the most likely pose for the robot is now given a new set of laser data,

is probably close to where the odometry predicts the robot to be.

This prediction means that we can refine our search to poses near the prediction

and be more confident in the validity of our search results.

We measure each pose p in the search based on a map registration metric.

One metric is to consider the sum of the map values m, at coordinates x and y,

where the laser returns r, hit.

This correlation metric can be modified to suit the application at hand.

In our case, the value of our map cell will be a log odds ratio, so

laser returns that are seen at a map location with high probability of

occupancy will strongly increase the registration in the metric score.

Laser returns with map locations known as free cells will decrease the metric score.

Additionally, the correlation can be scaled where returns from far

distances affect the metric calculation less than nearby the laser returns.

We register the robot on the map,

at the pose that maximizes the registration metric.

Thus, when the odometry is calculated,

it uses this pose to predict a new position of the robot, in time.

In addition to considering merely the laser returns, we can consider points for

the laser returns penetrated.

This calculation can further corroborate our map registration.

It requires considerably more computation however.

To capture pose uncertainty using a simple Gaussian on position and

angle may not provide a feasible approach.

In the next lecture, we will present a pose filter that can capture bi-modal

uncertainty and non-linear models and a computationally tractable way.