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**Advanced Mobile Robotics**

Dr. Jizhong Xiao Department of Electrical Engineering CUNY City College

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**Probabilistic Robotics**

Probabilistic Sensor Models Beam-based Model Likelihood Fields Model Feature-based Model

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Bayes Filter Reminder Prediction (Action) Correction (Measurement)

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**Sensors for Mobile Robots**

Contact sensors: Bumpers Internal sensors Accelerometers (spring-mounted masses) Gyroscopes (spinning mass, laser light) Compasses, inclinometers (earth magnetic field, gravity) Proximity sensors Sonar (time of flight) Radar (phase and frequency) Laser range-finders (triangulation, tof, phase) Infrared (intensity) Visual sensors: Cameras Satellite-based sensors: GPS

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Proximity Sensors The central task is to determine P(z|x), i.e., the probability of a measurement z given that the robot is at position x. Question: Where do the probabilities come from? Approach: Let’s try to explain a measurement.

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Noise Issues Sensors are imperfect! Specular Reflection

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**Beam-based Sensor Model**

Scan z consists of K measurements. Individual measurements are independent given the robot position and the map of environment. A cyclic array of k ultrasound sensors

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**Beam-based Sensor Model**

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**Typical Measurement Errors of an Range Measurements**

Beams reflected by obstacles Beams reflected by persons / caused by crosstalk Random measurements Maximum range measurements

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**Proximity Measurement**

Measurement can be caused by … a known obstacle. cross-talk. an unexpected obstacle (people, furniture, …). missing all obstacles (total reflection, glass, …). Noise is due to uncertainty … in measuring distance to known obstacle. in position of known obstacles. in position of additional obstacles. whether obstacle is missed.

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**Beam-based Proximity Model**

Measurement noise Unexpected obstacles zexp zmax zexp zmax Likelihood of sensing unexpected objects decreased with range Gaussian Distribution Exponential Distribution

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**Beam-based Proximity Model**

Random measurement Max range/Failures zexp zmax zexp zmax If fail to detect obstacle, report maximum range Uniform distribution Point-mass distribution

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**Resulting Mixture Density**

Weighted average, and How can we determine the model parameters? System identification method: maximum likelihood estimator (ML estimator)

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**Measured distances for expected distance of 300 cm.**

Raw Sensor Data Measured distances for expected distance of 300 cm. Sonar Laser

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**Approximation The likelihood of the data Z is given by**

Our goal is to identify intrinsic parameters that maximize this log likelihood Mathematic derivation can be found pp163~167 Psudo-code can be found in pp160 An iterative procedure for estimating parameters Deterministically compute the n-th parameter to satisfy normalization constraint.

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**Approximation Results**

Laser is more accurate than sonar, narrower Gaussians Laser The smaller range, the more accurate the measurement Sonar 400cm 300cm

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Example z P(z|x,m) Laser scan, projected into a previously acquired map The likelihood, evaluated for all positions and projected into the map. The darker a position, the larger P(z|x,m)

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**Summary Beam-based Model**

Assumes independence between beams. Justification? Overconfident! Suboptimal result Models physical causes for measurements. Mixture of densities for these causes. Assumes independence between causes. Problem? Implementation Learn parameters based on real data. Different models should be learned for different angles at which the sensor beam hits the obstacle. Determine expected distances by ray-tracing. Expected distances can be pre-processed.

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**Likelihood Fields Model**

Beam-based model is … not smooth for small obstacles and at edges (small changes of a robot’s pose can have a tremendous impact on the correct range of sensor beam, heading direction is particularly affected). not very efficient (computationally expensive in ray casting) Idea: Instead of following along the beam, just check the end point.

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**Likelihood Fields Model**

Project the end points of a sensor scan Zt into the global coordinate space of the map Probability is a mixture of … a Gaussian distribution with mean at distance to closest obstacle, a uniform distribution for random measurements, and a small uniform distribution for max range measurements. Again, independence between different components is assumed.

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**Likelihood Fields Model**

Distance to the nearest obstacles. Max range reading ignored

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**Example Example environment Likelihood field**

The darker a location, the less likely it is to perceive an obstacle P(z|x,m) Sensor probability Oi : Nearest point to obstacles O1 O2 O3 Zmax

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San Jose Tech Museum Occupancy grid map Likelihood field

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Scan Matching Extract likelihood field from scan and use it to match different scan. Sensor scan, 180 dots The darker a region, the smaller likelihood for sensing an object there

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**Properties of Likelihood Fields Model**

Highly efficient, uses 2D tables only. Smooth w.r.t. to small changes in robot position. Allows gradient descent, scan matching. Ignores physical properties of beams. Will it work for ultrasound sensors?

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**Additional Models of Proximity Sensors**

Map matching (sonar,laser): generate small, local maps from sensor data and match local maps against global model. (e.g., transform scans into occupancy maps) Scan matching (laser): map is represented by scan endpoints, match scan into this map. (e.g. ICP algorithm to stitch scans together) Features (sonar, laser, vision): Extract features such as doors, hallways from sensor data.

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**Features/Landmarks Active beacons (e.g., radio, GPS)**

Passive (e.g., visual, retro-reflective) Standard approach is triangulation Sensor provides distance, or bearing, or distance and bearing.

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Distance and Bearing

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**Feature-Based Measurement Model**

Feature vector is abstracted from the measurement: Sensors that measure range, bearing, & a signature (a numerical value, e.g., an average color) Conditional independence between features Feature-Based map: with i.e., a location coordinate in global coordinates & a signature Robot pose: Measurement model: Zero-mean Gaussian error variables with standard deviations

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**Sensor Model with Known Correspondence**

A key problem for range/bearing sensors: data association, (arise when the landmarks cannot be uniquely identified) Introduce a correspondence variable , between feature and landmark If , then the i-th feature observed at time t corresponds to the j-th landmark in the map Algorithm for calculating the probability of a landmark measurement: inputs: feature , with know correspondence , the robot pose and the map, Its output is the numerical probability

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**Summary of Sensor Models**

Explicitly modeling uncertainty in sensing is key to robustness. In many cases, good models can be found by the following approach: Determine parametric model of noise free measurement. Analyze sources of noise. Add adequate noise to parameters (eventually mix in densities for noise). Learn (and verify) parameters by fitting model to data. Likelihood of measurement is given by “probabilistically comparing” the actual with the expected measurement. This holds for motion models as well. It is extremely important to be aware of the underlying assumptions!

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Thanks Homework 4, 5 posted

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