This page is generated by generate_example_gallery.py from the demo images under src/simulations and updated automatically after merge. Do not edit it by hand — edit the docstring of the simulation script instead.
Compares EKF, UKF and Particle Filter localization with the same motion model, GNSS observations and noise
Author: Sahruday Patti

Compares EKF and UKF localization with GNSS observations
Author: Bruno DOKPOMIWA

Estimates the vehicle pose by fusing a motion model and GNSS observations with an Extended Kalman Filter
Author: Shisato Yano

Estimates the vehicle pose with weighted samples (particles) and GNSS observations
Author: Sahruday Patti

Estimates the vehicle pose with an Unscented Kalman Filter using sigma points and GNSS observations
Author: Bruno DOKPOMIWA

Builds a grid map that marks each cell as occupied or free from LiDAR point clouds
Author: Shisato Yano, Bhavesh Lokesh Agarwal

Builds a grid map that assigns a cost to each cell from LiDAR point clouds
Author: Bhavesh Lokesh Agarwal

Builds a map of normal distributions (mean and covariance) per grid cell from LiDAR point clouds
Author: Shisato Yano

Builds a potential field map from LiDAR point clouds, with high potential around obstacles
Author: Panav Arpit Raaj

Builds a polar obstacle density histogram around the vehicle from LiDAR point clouds
Author: Khushi

Ant Colony Optimization: ants search the grid and reinforce shorter paths with pheromones
Author: Banaan Kiamanesh

Runs A* search from both the start and the goal until the two searches meet
Author: Erwin Lejeune


A* search over continuous vehicle poses using motion primitives that respect the minimum turning radius
Author: Shreyansh Shethia


Grid-based shortest path search guided by a heuristic distance to the goal
Author: Shantanu Parab


Grid-based shortest path search that expands the lowest-cost node first
Author: Ashish Varma


Incremental replanning when a new obstacle appears on the planned path
Author: Erwin Lejeune


Smooths an A* path by treating it as an elastic band pushed away from obstacles
Author: Erwin Lejeune


Genetic Algorithm: evolves candidate paths and smooths the best one with a spline
Author: Banaan Kiamanesh

Plans a path by descending the gradient of a potential field toward the goal
Author: Panav Arpit Raaj

RRT* that samples only inside an ellipse once a first path is found, to converge faster
Author: Rajat Arora


Samples random nodes, connects them into a roadmap and searches it with Dijkstra
Author: Erwin Lejeune


Particle Swarm Optimization: a swarm of candidate paths converges toward the best one
Author: Erwin Lejeune


Learns a Q-table policy on the grid by reinforcement learning and follows its greedy path
Author: Banaan Kiamanesh

Grows a random tree from the start until it reaches the goal
Author: Shantanu Parab


Grows RRT* trees from both the start and the goal and connects them
Author: Erwin Lejeune


RRT that rewires the tree to shorten paths as more samples are added
Author: Erwin Lejeune


Pure Pursuit whose look-ahead distance shortens on sharp turns and lengthens on straights
Author: Erwin Lejeune
![]()
Steering control that minimizes a quadratic cost of tracking error and control input
Author: Shisato Yano
![]()
Optimizes future inputs over a prediction horizon with do-mpc and shows the predicted trajectory
Author: Mohit Kumar
![]()
Samples many input sequences, weights them by cost, and shows all evaluated trajectories
Author: Erwin Lejeune
![]()
Steers toward a look-ahead point on the course
Author: Shisato Yano
![]()
Steering control based on the lateral and heading errors at the rear wheel
Author: Shisato Yano
![]()
Steering control based on the heading error and the lateral error at the front wheel
Author: Shisato Yano
![]()
Simulates an omni-directional LiDAR that detects moving and static obstacles as point clouds
Author: Shisato Yano

Fits a rectangle to each obstacle's point cloud by L-shape fitting
Fits a rectangle to each obstacle's point cloud by robust least squares
Author: Shisato Yano, Shreyansh Shethia


Finds the nearest source point for each target point with a kd-tree
Finds all source points within a radius of each target point with a kd-tree
Author: Shisato Yano


Estimates the LiDAR's mounting position and angle on the vehicle with an Unscented Kalman Filter
Author: Shisato Yano

Builds a 2D cubic spline course and computes its yaw angle and curvature
Interpolates sample points with a 1D cubic spline
Author: Shisato Yano



