Simultaneous Localisation and Mapping in AD & ADAS
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Transcript of 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