Memory Optimization in Embedded Systems: From 40MB to 2MB

Memory Optimization in Embedded Systems: From 40MB to 2MB

The Challenge

When working on large-scale robotics applications at, we faced a critical bottleneck: our ROS2 nodes were consuming excessive memory when processing large map data. The system needed to handle dynamic map loading for autonomous navigation, but our initial implementation loaded entire maps into RAM, consuming 40MB+ for production environments.

Understanding the Problem

The root cause was architectural: we were using naive data structures that did not account for spatial locality or access patterns. Every map segment was stored in memory regardless of whether the robot would ever visit that area.

The Solution: Segmentation and Lazy Loading

I implemented a three-tier caching strategy:

1. Active Zone: Only segments within 10m of current position

2. Prefetch Zone: Segments 10-30m ahead based on predicted path

3. Disk Cache: All other segments stored on persistent storage

Key Implementation Details

Spatial Indexing with Quadtrees: Using quadtrees for O(log n) lookup instead of O(n) linear search. This data structure divides 2D space into four quadrants recursively, enabling efficient spatial queries.

Predictive Prefetching: Predict next 5 waypoints based on trajectory and prefetch segments along the predicted path. This reduces cache misses by anticipating robot movement patterns.

Results

Key Learnings

1. Profile First: Use tools like Valgrind to identify actual bottlenecks

2. Data Structure Matters: Choosing quadtrees over linear arrays was crucial

3. Predictive Algorithms: Understanding robot behavior enabled intelligent prefetching

4. Trade-offs: We traded disk I/O for memory, which was the right choice for embedded systems

This optimization demonstrates how deep understanding of algorithms and data structures can solve real-world constraints in embedded systems.