Simultaneous Localisation and Mapping in AD & ADAS
-
Upload
globallogic-ukraine -
Category
Software
-
view
289 -
download
0
Transcript of Simultaneous Localisation and Mapping in AD & ADAS
![Page 1: Simultaneous Localisation and Mapping in AD & ADAS](https://reader034.fdocuments.net/reader034/viewer/2022042513/5aac10337f8b9adb278b4685/html5/thumbnails/1.jpg)
1
SLAM in AD & ADAS
Igor Uspeniev, Oleksandr Lutsiv-Shumskyi
December 2017
![Page 2: Simultaneous Localisation and Mapping in AD & ADAS](https://reader034.fdocuments.net/reader034/viewer/2022042513/5aac10337f8b9adb278b4685/html5/thumbnails/2.jpg)
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
![Page 3: Simultaneous Localisation and Mapping in AD & ADAS](https://reader034.fdocuments.net/reader034/viewer/2022042513/5aac10337f8b9adb278b4685/html5/thumbnails/3.jpg)
Sensors
![Page 4: Simultaneous Localisation and Mapping in AD & ADAS](https://reader034.fdocuments.net/reader034/viewer/2022042513/5aac10337f8b9adb278b4685/html5/thumbnails/4.jpg)
Autonomous Vehicle: Functional Steps
Environmental reconstructionSensors Act
![Page 5: Simultaneous Localisation and Mapping in AD & ADAS](https://reader034.fdocuments.net/reader034/viewer/2022042513/5aac10337f8b9adb278b4685/html5/thumbnails/5.jpg)
55
Environmental Reconstruction
![Page 6: Simultaneous Localisation and Mapping in AD & ADAS](https://reader034.fdocuments.net/reader034/viewer/2022042513/5aac10337f8b9adb278b4685/html5/thumbnails/6.jpg)
Environmental Reconstruction Steps
Structure From Motion → Texture mapping → Object Recognition
![Page 7: Simultaneous Localisation and Mapping in AD & ADAS](https://reader034.fdocuments.net/reader034/viewer/2022042513/5aac10337f8b9adb278b4685/html5/thumbnails/7.jpg)
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
![Page 8: Simultaneous Localisation and Mapping in AD & ADAS](https://reader034.fdocuments.net/reader034/viewer/2022042513/5aac10337f8b9adb278b4685/html5/thumbnails/8.jpg)
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
![Page 9: Simultaneous Localisation and Mapping in AD & ADAS](https://reader034.fdocuments.net/reader034/viewer/2022042513/5aac10337f8b9adb278b4685/html5/thumbnails/9.jpg)
99
Simultaneous Localization And Mapping (SLAM)
![Page 10: Simultaneous Localisation and Mapping in AD & ADAS](https://reader034.fdocuments.net/reader034/viewer/2022042513/5aac10337f8b9adb278b4685/html5/thumbnails/10.jpg)
Simultaneous Localization And Mapping (SLAM)
From frames image processing to global feature map and self movement
![Page 11: Simultaneous Localisation and Mapping in AD & ADAS](https://reader034.fdocuments.net/reader034/viewer/2022042513/5aac10337f8b9adb278b4685/html5/thumbnails/11.jpg)
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.
![Page 12: Simultaneous Localisation and Mapping in AD & ADAS](https://reader034.fdocuments.net/reader034/viewer/2022042513/5aac10337f8b9adb278b4685/html5/thumbnails/12.jpg)
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
![Page 13: Simultaneous Localisation and Mapping in AD & ADAS](https://reader034.fdocuments.net/reader034/viewer/2022042513/5aac10337f8b9adb278b4685/html5/thumbnails/13.jpg)
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
![Page 14: Simultaneous Localisation and Mapping in AD & ADAS](https://reader034.fdocuments.net/reader034/viewer/2022042513/5aac10337f8b9adb278b4685/html5/thumbnails/14.jpg)
Typical SLAM system
![Page 15: Simultaneous Localisation and Mapping in AD & ADAS](https://reader034.fdocuments.net/reader034/viewer/2022042513/5aac10337f8b9adb278b4685/html5/thumbnails/15.jpg)
Feature detection
![Page 16: Simultaneous Localisation and Mapping in AD & ADAS](https://reader034.fdocuments.net/reader034/viewer/2022042513/5aac10337f8b9adb278b4685/html5/thumbnails/16.jpg)
Feature detection
Corner detection. Corners are easy to distinguish
Monotonic region Edge. No
changes along it
Corner. Changes
in any direction
![Page 17: Simultaneous Localisation and Mapping in AD & ADAS](https://reader034.fdocuments.net/reader034/viewer/2022042513/5aac10337f8b9adb278b4685/html5/thumbnails/17.jpg)
Feature detection
Harris corner
detector results
![Page 18: Simultaneous Localisation and Mapping in AD & ADAS](https://reader034.fdocuments.net/reader034/viewer/2022042513/5aac10337f8b9adb278b4685/html5/thumbnails/18.jpg)
Feature detection
Blob detection:
adds invariance to
scale
![Page 19: Simultaneous Localisation and Mapping in AD & ADAS](https://reader034.fdocuments.net/reader034/viewer/2022042513/5aac10337f8b9adb278b4685/html5/thumbnails/19.jpg)
Feature description and tracking
Describe detected
points so that
correspondence
can be found
![Page 20: Simultaneous Localisation and Mapping in AD & ADAS](https://reader034.fdocuments.net/reader034/viewer/2022042513/5aac10337f8b9adb278b4685/html5/thumbnails/20.jpg)
Back-end
Perception
Filtering
(RANSAC, etc.)Motion
Map
(internal+external)Localization
Semantic analysis Correction
![Page 21: Simultaneous Localisation and Mapping in AD & ADAS](https://reader034.fdocuments.net/reader034/viewer/2022042513/5aac10337f8b9adb278b4685/html5/thumbnails/21.jpg)
Loop closing
Recognizing an already mapped area to
improve our estimate of map and robot
location.
![Page 22: Simultaneous Localisation and Mapping in AD & ADAS](https://reader034.fdocuments.net/reader034/viewer/2022042513/5aac10337f8b9adb278b4685/html5/thumbnails/22.jpg)
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
![Page 23: Simultaneous Localisation and Mapping in AD & ADAS](https://reader034.fdocuments.net/reader034/viewer/2022042513/5aac10337f8b9adb278b4685/html5/thumbnails/23.jpg)
SLAM Example. EKF SLAM
Prediction
Correction
The Kalman filter provides a solution
to the online SLAM problem
![Page 24: Simultaneous Localisation and Mapping in AD & ADAS](https://reader034.fdocuments.net/reader034/viewer/2022042513/5aac10337f8b9adb278b4685/html5/thumbnails/24.jpg)
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.
![Page 25: Simultaneous Localisation and Mapping in AD & ADAS](https://reader034.fdocuments.net/reader034/viewer/2022042513/5aac10337f8b9adb278b4685/html5/thumbnails/25.jpg)
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
![Page 26: Simultaneous Localisation and Mapping in AD & ADAS](https://reader034.fdocuments.net/reader034/viewer/2022042513/5aac10337f8b9adb278b4685/html5/thumbnails/26.jpg)
Some SLAM Problems
rain
poor lighting
dynamic
environment
no road surface marking
![Page 27: Simultaneous Localisation and Mapping in AD & ADAS](https://reader034.fdocuments.net/reader034/viewer/2022042513/5aac10337f8b9adb278b4685/html5/thumbnails/27.jpg)
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.
![Page 28: Simultaneous Localisation and Mapping in AD & ADAS](https://reader034.fdocuments.net/reader034/viewer/2022042513/5aac10337f8b9adb278b4685/html5/thumbnails/28.jpg)
SLAM Case Studies. ORB-SLAM Static Environment
![Page 29: Simultaneous Localisation and Mapping in AD & ADAS](https://reader034.fdocuments.net/reader034/viewer/2022042513/5aac10337f8b9adb278b4685/html5/thumbnails/29.jpg)
SLAM Case Studies. ORB Dynamic Environment
DE Overcoming:
● Feature set
refresh
● Feature uniform
distribution
● 3D feature
labeling
● SIFT with
CUDA
![Page 30: Simultaneous Localisation and Mapping in AD & ADAS](https://reader034.fdocuments.net/reader034/viewer/2022042513/5aac10337f8b9adb278b4685/html5/thumbnails/30.jpg)
30
Thank you