simultaneous localisation and mapping in ad & adas

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SLAM in AD & ADAS

Igor Uspeniev, Oleksandr Lutsiv-Shumskyi

December 2017

City Traffic Movement

The car moves in difficult road conditions with surrounding obstacles, requiring localization, recognition and prediction.

● Complex measurements

● Dynamic scene

● Realtime requirements● Critical to life risks● Road rules and management● Computation load limits

Sensors

Autonomous Vehicle: Functional Steps

Environmental reconstructionSensors Act

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Environmental Reconstruction

Environmental Reconstruction Steps

Structure From Motion → Texture mapping → Object Recognition

Structure From Motion

❏ Environment measurement with movement allows to reconstruct 3D model of objects for accurate and timely interaction with them

❏ Sensor data fusion for high accuracy reconstruction

Sensors + Movement --> Localization + Environment

Object Recognition

● 2D image patterns● 3D voxel patterns● Combined approaches

Problems

● Dataset combinatorial explosion● Computation load● Object separation● Incomplete object observing● Light, dirt, weather influence● Critical time requirements

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Simultaneous Localization And Mapping (SLAM)

Simultaneous Localization And Mapping (SLAM)

From frames image processing to global feature map and self movement

The task of SLAM

Given a Robot with sensor set, at the same time:

● Construct a model (the Map) of the

environment.

● Estimate the State of the robot (pose,

velocity, etc.) in the Map

SLAM is chicken-or-egg problem.

SLAM generations and researchers

“Ages” in SLAM development:

1. 1986-2004 Classical age. Extended Kalman Filters, Particle

Filters and maximum likelihood estimation approaches.

2. 2004-2015 Algorithmic-analysis age. Study of fundamental

properties, including observability, convergence, consistency.

3. 2015 - now Robust-perception age:

● robust performance

● high-level understanding

● resource awareness

● task-driven perception

Cyrill Stachniss

Davide Scaramuzza

Ideal environment for SLAM in automotive

● Well observable environment

● Sensors availability without

degradation

● Good road surface marking

● Static environment

● Slow movement on road

● Precise map

Typical SLAM system

Feature detection

Feature detection

Corner detection. Corners are easy to distinguish

Monotonic region Edge. No

changes along it

Corner. Changes

in any direction

Feature detection

Harris corner

detector results

Feature detection

Blob detection:

adds invariance to

scale

Feature description and tracking

Describe detected

points so that

correspondence

can be found

Back-end

Perception

Filtering

(RANSAC, etc.)Motion

Map

(internal+external)Localization

Semantic analysis Correction

Loop closing

Recognizing an already mapped area to

improve our estimate of map and robot

location.

SLAM Example. EKF SLAM

Given

● The robot’s controls u1:T = {u1, u2, u3, …, uT}

● Observations z1:T = {z1, z2, z3, …, zT}

Wanted

● Map of the environment m

● Path of the robot x0:T = {x0, x1, x2, …, xT}

Map Path

Controls Observations

SLAM

SLAM Example. EKF SLAM

Prediction

Correction

The Kalman filter provides a solution

to the online SLAM problem

Some SLAM Problems: Robustness

Static world assumption may Not

hold in Short Term:

● Moving objects, e.g. car,

pedestrians, etc.

Some approaches:

● Filter out dynamic objects at

front-end: Object Recognition

● Use robust optimization back-

end.

Some SLAM Problems: Robustness

Static world assumption may Not

hold in Long Term:

● Light and weather change

● Seasonal change

Some approaches:

● Use light independent

descriptors.

● Create rich maps with semantic

meaning: Object Recognition

Some SLAM Problems

rain

poor lighting

dynamic

environment

no road surface marking

Some SLAM Problems: Scalability

Open problems:

● How to Efficiently store Map in long term?

● How often to update map in long term?

● Optimization of SLAM for resource-constrained platforms.

SLAM Case Studies. ORB-SLAM Static Environment

SLAM Case Studies. ORB Dynamic Environment

DE Overcoming:

● Feature set

refresh

● Feature uniform

distribution

● 3D feature

labeling

● SIFT with

CUDA

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Thank you