F1/10 th Autonomous Racing. Localization. Nischal K N
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1 F1/10 th Autonomous Racing Localization Nischal K N
2 System Overview Mapping Hector Mapping Localization Path Planning Control
3 System Overview Mapping Hector Mapping Localization Adaptive Monte Carlo Localization Path Planning Control
4 ? Where am I?????
5 ? Where am I????? Position & Orientation
6 Localization using Odometry Car Trajectory Initial Car Position Free Space Walls
7 Drawbacks of Localization using Wheel Odometry Wheel spin due to lack of traction
8 Drawbacks of Localization using Hector odometry Failed scan matching due to lack of features
9 Issue A mechanism to compensate the mistakes committed by odometry A solution robust to compensate for lack of information on initial position
10 Issue A mechanism to compensate the mistakes committed by odometry A solution robust to compensate for lack of information on initial position Solution: Monte Carlo Localization Alternate Solutions: Kalman Filter, Topological Markov Localization
11 Robot Particle Filter A Example in 1 Dimension Door Belief State
12 Robot Particle Filter A Example in 1 Dimension Door Direction of motion Belief State
13 Particle Filter At time t = 1 Robot A Example in 1 Dimension Door Direction of motion
14 Particle Filter At time t = 1 Robot A Example in 1 Dimension Door Direction of motion Measurement Model
15 Particle Filter At time t = 1 Robot A Example in 1 Dimension Door Direction of motion Measurement Model Belief State
16 At time t = 2, robot moves forward a certain distance
17 At time t = 2, robot moves forward a certain distance Motion Model update
18 At time t = 2, robot moves forward a certain distance Motion Model update Measurement model
19 At time t = 2, robot moves forward a certain distance Motion Model update Measurement model Belief State
20 Continuous State
21 Continuous State Discrete State
22 Continuous State Discrete State
23 Particle Filter in 2D
24 Particle Filter in 2D Odometry pose
25 Particle Filter in 2D
26 Scan Correlation
27 Scan Correlation S = σ m σ n (Amn A)(Bmn B) σ m σ n A mn A 2 σ m σ n B mn B 2
28 Scan Correlation S = σ m σ n (Amn A)(Bmn B) σ m σ n A mn A 2 σ m σ n B mn B 2 Particle Weight Particle 1 S 1
29 Scan Correlation S = σ m σ n (Amn A)(Bmn B) σ m σ n A mn A 2 σ m σ n B mn B 2 Particle Weight Particle 1 S 1 Particle 2 S 2
30 Scan Correlation S = σ m σ n (Amn A)(Bmn B) σ m σ n A mn A 2 σ m σ n B mn B 2 Particle Weight Particle 1 S 1 Particle 2 S 2 Particle 3 S 3
31 Scan Correlation S = σ m σ n (Amn A)(Bmn B) σ m σ n A mn A 2 σ m σ n B mn B 2 Particle Weight Particle 1 S 1 Particle 2 S 2 Particle 3 S 3 Particle 4 S 4
32 Scan Correlation S = σ m σ n (Amn A)(Bmn B) σ m σ n A mn A 2 σ m σ n B mn B 2 Particle Weight Particle 1 S 1 Particle 2 S 2 Particle 3 S 3 Particle 4 S 4 Particle 5 S 5
33 Scan Correlation S = σ m σ n (Amn A)(Bmn B) σ m σ n A mn A 2 σ m σ n B mn B 2 Particle Weight Particle 1 S 1 Particle 2 S 2 Particle 3 S 3 Particle 4 S 4 Particle 5 S 5 Particle 6 S 6
34 Scan Correlation S = σ m σ n (Amn A)(Bmn B) σ m σ n A mn A 2 σ m σ n B mn B 2 Particle Weight Particle 1 S 1 Particle 2 S 2 Particle 3 S 3 Particle 4 S 4 Particle 5 S 5 Particle 6 S 6
35 Localization using Odometry Particle Filter
36 Update step Update the particle cloud with the update in position from the odometry Repeat Scan matching process for each particle and determine the weights.
37 Update step Odometry update Update the particle cloud with the update in position from the odometry Repeat Scan matching process for each particle and determine the weights.
38 Update step Odometry update Update the particle cloud with the update in position from the odometry Repeat Scan matching process for each particle and determine the weights. Particle Weights W t W t 1 S
39 Weights Particle Filter without Resampling Particles
40 Resampling Original Particles After N iterations Resampling
41 Resampling Original Particles After N iterations Resampling
42 Resampling Original Particles After N iterations Resampling
43 Particles
44 Weights Particle filter with Resampling Particles
45 Kullback Leibler divergence (KLD Sampling) Variable Particle size Sample size is proportional to error between odometry position and sample based approximation i.e smaller sample size when particles have converged
46 Particle Filters in ROS Adaptive Monte Carlo Localization Package Localization for a robot moving in a 2D space Localizes against a pre-existing map
47 Tf tree Where does AMCL fit in world_frame map odom base_frame
48 Tf tree Where does AMCL fit in world_frame map odom Odometry (Hector) base_frame
49 Tf tree Where does AMCL fit in world_frame map odom base_frame Odometry Drift Odometry (AMCL) (Hector)
50 Input and Output Parameters
51 Input and Output Parameters Input Parameters: 1. Laser Scan
52 Input and Output Parameters Input Parameters: 1. Laser Scan 2. Dead Reckoning/Odometry
53 Input and Output Parameters Input Parameters: 1. Laser Scan 2. Dead Reckoning/Odometry 3. Map
54 Input and Output Parameters Input Parameters: 1. Laser Scan 2. Dead Reckoning/Odometry 3. Map Output Parameters: 1. AMCL pose
55 Input and Output Parameters Input Parameters: 1. Laser Scan 2. Dead Reckoning/Odometry 3. Map Output Parameters: 1. AMCL pose 2. Particle Cloud
56 Video of AMCL particles
57 min_particles Default: 100 AMCL Parameters The minimum number of particles to be used for calculating correlation max_particles Default: 500 The maximum number of particles to be used for calculating correlation
58 update_min_d Default: 0.2m AMCL Parameters The minimum translation movement required by the vehicle before an pose update is published update_min_a Default: π Τ6 radians The minimum angular movement required by the vehicle before an pose update is published
59 AMCL Parameters initial_pose_x Default: 0 initial_pose_y Default: 0 initial_pose_a Default: 0 The initial mean position of the particles to initialize the particle filter
60 AMCL Parameters initial_cov_xx Default: 0 initial_cov_yy Default: 0 initial_cov_aa Default: 0 The covariance of particles distributed around the mean
61 What Next? Path Planning and Trajectory Generation Cost Maps Control Algorithms For Navigation
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