The car uses LIDAR and Camera data to autonomously drive in the desired path. Algorithms for map generation, localization, and detection developed on AURIX TC297 using data from RP-LIDAR and OpenMV Camera.

1. Drive the Maze02
ProjectforSummerTerm2019

2. Motivation
 Autonomous driving is in the forefront of key technologies of
this decade. The motivation behind this is to improve safety
and efficiency.
 Autonomous driving technology can also be adapted to indoor
envornments for applications like autonomous robots.
 This technology can also be extended to autonomous aerial
vehicles thus presenting us with infinite opportunities to
improve lives in unimaginable ways.
 This growing technology has attracted lot of investment in
corresponding Research and Development.

3. Project Goal
 Develop a navigation algorithm for an autonomous car
mounted with LiDAR and Camera sensors to identify obstacles
and their color in a maze and traverse around them as per the
user defined sequence.
 Integrate a camera sensor (on OpenMV) and create a gateway
for communication between camera module and Aurix.
 Implicit
1 : Develop algorithms for simultaneous localisation and
mapping.
2 : Develop algorithms for sensor data fusion.
3 : Develop algorithms for path planning.

4. Risks
Organizational
 Communication among
teams.
 Resource availability – Student
car.
Technical
 No prior experience with
camera.
 Inaccuracy of Sensors.

5. System architecture

6. State machine

7. Car Localization
Localization Initialization.
Current position of the car inside the maze.
Functions to Support controlling of the car
inside the maze
Moving car to a specific co-ordinate (x, y)
Rotating car to an angle theta.

8. Car Initialization

9. Car Localization

10. Obstacle Detection
• Calculate distance between
consecutive lidar samples
• samples on walls will be close
to each other and samples on
obstacle will be far from the
previous wall sample..
• Set a realistic thereshold and
segment the obstacle

11. 2-D transformation for obstacle mapping
• Obstacle location is determined
with respect to Car location.
• Car location with respect to
origin can be found using car
localisation algorithm.
• Using above data and with the
help of 2-d transformations,
obstacle location can be
determined w.r.t Origin.

12. Distance between samples on walls increases as the distance between car
and wall increases.
If car is located at a long disatnce, a resolution of 5degrees increases the
distance between samples on walls greater than the 40cm (which is the
minimum distance between walls and obstacle as per specification).
This affects the threshold. A shorter threshold may detect fake obstacles and
a larger threshold may not detect obstacles closer to wall.
Limitations and improvements

13. Lidar resolution is set to 5 degrees which decreases the accuracy while
calculating car orientation.
Improvement : Lidar resolution can be increased
Controlling is based on polling . Hence moving car with high speed leads to
inaccuracy in reaching destination.
Limitations and improvements

14. Mapping and Color Tagging :
• Car in the rest position –
before color tagging .

15. Mapping and Color Tagging :
• Car aligned with respect to
obstacle for tagging the
color.

16. Mapping and Color Tagging :
• Car aligned with respect to
next obstacle for tagging
the color.

17. Scanning Positions Algorithm:
• Based on the Maze dimensions the
Scanning positions are calculated.
• The Scanning positions are
reordered in order to reach the
nearest position.
• For a 5x5 Maze 4 scanning
positions will be considered in
order to identify most of the
obstacles.

20. Path planning

21. Algorithm -PathPlanning
 To keep the functionality simple and to reduce
the memory and processor consumption, a
static path planning is done rather than a
dynamic planning.
 Traversal coordinates are generated around the
obstacles based on the location of obstacle.
 Three coordinates are generated on one
particular side of the obstacle, to traverse
around the obstacle based on user sequence.

22. Algorithm
 Once the coordinates are generated, the
obstacle are traversed in the user defined
sequence.

23. Future scope
 Advanced Camera setup.
 Improved Lidar Sensor Data Resolution for better
accuracy.
 Need for more advanced processors for
processing Navigation algorithms .
 Better SLAM algorithms could be used for
navigating outside the Maze environment.