LiDAR-based SLAM (gmapping, cartographer) in ROS - Performance & Optimization

Performance: LiDAR-based SLAM (gmapping, cartographer)

HIGH IMPACT

This concept affects the real-time mapping and localization speed in robotic systems, impacting how quickly and smoothly the robot can build and update maps while moving.

Building and updating a 2D map in real-time using LiDAR data

ROS

roslaunch gmapping slam_gmapping.launch scan:=/scan particles:=30 update_interval:=1.0
# Tuned parameters for balanced CPU load and update speed

Lower particle count and optimized update interval reduce CPU load and speed up map updates, improving responsiveness.

📈 Performance GainCPU usage reduced by 40%, map updates occur within 50ms, smoother robot navigation

Building and updating a 2D map in real-time using LiDAR data

ROS

roslaunch gmapping slam_gmapping.launch scan:=/scan
# Using default parameters with high particle count and slow update rates

High particle count and slow update rates cause heavy CPU usage and delayed map updates, leading to laggy robot response.

📉 Performance CostCPU usage spikes causing delayed sensor processing and map refreshes, increasing latency by 200-300ms

Performance Comparison

Pattern	CPU Usage	Memory Usage	Latency	Verdict
High particle count with default gmapping	High (80-100%)	Moderate (200MB+)	High (200-300ms)	[X] Bad
Tuned gmapping with lower particles	Moderate (40-60%)	Moderate (150MB)	Low (50ms)	[OK] Good
Cartographer default high-res grid	Very High (90-100%)	High (500MB+)	Very High (500ms+)	[X] Bad
Cartographer with filtering and optimized grid	Moderate (50-70%)	Moderate (300MB)	Low (100ms)	[OK] Good

Rendering Pipeline

LiDAR SLAM processes sensor data through stages of data acquisition, scan matching, map updating, and pose estimation. Each stage impacts CPU and memory usage, affecting real-time performance.

→Data Acquisition

→Scan Matching

→Map Update

→Pose Estimation

⚠️ BottleneckScan Matching stage is most CPU intensive due to complex calculations comparing current scans to the map.

Optimization Tips

1Lower particle count in gmapping to reduce CPU load.

2Apply sensor data filtering to reduce processing volume.

3Optimize map resolution to balance detail and performance.

Performance Quiz - 3 Questions

What is the main cause of high CPU usage in LiDAR-based SLAM algorithms like gmapping?

A High particle count and complex scan matching

B Low sensor data resolution

C Using filtered sensor data

D Reducing map update frequency

DevTools: rosbag play with rqt_graph and rqt_runtime_monitor

How to check: 1. Record sensor and SLAM node data using rosbag. 2. Use rqt_runtime_monitor to observe CPU and memory usage of SLAM nodes. 3. Use rqt_graph to verify data flow and node update rates.

What to look for: Look for CPU spikes during scan matching, memory usage trends, and delays in map update topics to identify performance bottlenecks.

Practice

1. What is the main purpose of LiDAR-based SLAM in ROS?

easy

A. To process camera images for object detection

B. To control robot motors directly

C. To help robots create maps and know their location using laser scans

D. To send commands to robot arms

Solution

1. Step 1: Understand SLAM basics

   SLAM stands for Simultaneous Localization and Mapping, which means building a map and knowing the robot's position at the same time.

2. Step 2: Identify LiDAR role

   LiDAR uses laser scans to sense the environment, which helps SLAM create accurate maps and localize the robot.

3. Final Answer:

   To help robots create maps and know their location using laser scans -> Option C

4. Quick Check:

   LiDAR-based SLAM = mapping + localization with lasers [OK]

Hint: SLAM = map + location using sensors like LiDAR [OK]

Common Mistakes:

• Confusing SLAM with motor control
• Thinking SLAM processes camera images only
• Assuming SLAM sends commands to robot arms

2. Which ROS launch command correctly starts the gmapping SLAM node?

easy

A. rosstart gmapping slam.launch

B. roslaunch gmapping slam_gmapping.launch

C. rosrun gmapping slam_gmapping.py

D. roslaunch cartographer slam.launch

Solution

1. Step 1: Recall ROS launch syntax

   ROS uses roslaunch to start nodes with launch files, specifying package and launch file name.

2. Step 2: Match gmapping launch file

   The gmapping package provides slam_gmapping.launch to start the SLAM node.

3. Final Answer:

   roslaunch gmapping slam_gmapping.launch -> Option B

4. Quick Check:

   Use roslaunch + package + launch file [OK]

Hint: Use roslaunch with package and launch file name [OK]

Common Mistakes:

• Using rosrun instead of roslaunch for launch files
• Typing rosstart which is invalid
• Confusing cartographer launch with gmapping

3. Given this ROS launch snippet for Cartographer:

<launch>
  <include file="$(find cartographer_ros)/launch/demo_backpack_2d.launch" />
</launch>

What will happen when you run this launch file?

medium

A. It launches a robot simulation without SLAM

B. It starts gmapping SLAM with a 3D map

C. It causes a syntax error due to missing parameters

D. It starts Cartographer SLAM with a 2D LiDAR backpack demo configuration

Solution

1. Step 1: Understand the include tag

   The <include> tag loads another launch file from the cartographer_ros package.

2. Step 2: Identify the included launch file

   demo_backpack_2d.launch is a Cartographer demo for 2D SLAM using a backpack LiDAR sensor.

3. Final Answer:

   It starts Cartographer SLAM with a 2D LiDAR backpack demo configuration -> Option D

4. Quick Check:

   Include demo_backpack_2d.launch = Cartographer 2D SLAM [OK]

Hint: Include demo_backpack_2d.launch means Cartographer 2D SLAM [OK]

Common Mistakes:

• Confusing Cartographer with gmapping
• Assuming missing parameters cause errors here
• Thinking it launches simulation without SLAM

4. You try to run gmapping with this command:

roslaunch gmapping slam_gmapping.launch

But get an error: "Could not find node of type slam_gmapping in package gmapping". What is the likely cause?

medium

A. The gmapping package is not installed or sourced properly

B. You used rosrun instead of roslaunch

C. The launch file name is misspelled

D. Your LiDAR sensor is not connected

Solution

1. Step 1: Analyze the error message

   The error says it cannot find the node type in the gmapping package, meaning the package or node is missing.

2. Step 2: Check common causes

   This usually happens if the gmapping package is not installed or the ROS environment is not sourced to find it.

3. Final Answer:

   The gmapping package is not installed or sourced properly -> Option A

4. Quick Check:

   Missing package or source = node not found error [OK]

Hint: Check package installed and sourced if node not found error [OK]

Common Mistakes:

• Assuming rosrun vs roslaunch causes this error
• Thinking a typo in launch file name causes node type error
• Blaming LiDAR connection for node not found

5. You want to create a 3D map using Cartographer with a LiDAR sensor on a robot. Which approach is best?

hard

A. Use Cartographer's 3D SLAM launch files and configure sensor topics properly

B. Use gmapping with default 2D settings and add a 3D camera

C. Run gmapping and Cartographer simultaneously for better accuracy

D. Use Cartographer's 2D demo launch files and convert output to 3D later

Solution

1. Step 1: Understand SLAM capabilities

   Gmapping supports only 2D SLAM, so it cannot create 3D maps directly.

2. Step 2: Choose Cartographer for 3D

   Cartographer supports 3D SLAM and has launch files designed for 3D mapping with LiDAR sensors.

3. Step 3: Configure sensor topics

   Properly setting sensor topics in Cartographer launch files ensures correct data flow for 3D mapping.

4. Final Answer:

   Use Cartographer's 3D SLAM launch files and configure sensor topics properly -> Option A

5. Quick Check:

   3D map needs Cartographer 3D launch + sensor config [OK]

Hint: 3D mapping needs Cartographer 3D launch and sensor setup [OK]

Common Mistakes:

• Trying to use gmapping for 3D maps
• Running both SLAM systems at once
• Using 2D demos and expecting 3D output