$ show JuliaRobotics/Caesar.jl
Caesar.jl is a Julia package for factor graph-based simultaneous localization and mapping (SLAM) and state estimation. It provides a framework for building factor graphs with variables (poses, landmarks) and factors (constraints), solving via incremental inference (iSAM2) and Bayes tree, and retrieving beliefs for non-Gaussian and multimodal distributions. The library supports integration with various sensors (AprilTags, lidar, sonar, GPS, IMU) through custom factors and middleware (ROS, LCM, ZMQ). Examples demonstrate applications in marine (ASV, AUV, ROV), wheeled (racecar, Victoria Park), tracking (acoustic, radar), and learning (neural network factors) domains. The architecture is modular, with core packages (RoME, IncrementalInference, DistributedFactorGraphs) handling specific aspects of the SLAM pipeline.
The LLM identified 5 files worth highlighting for reuse:
A persistent dictionary that stores values as files in a directory, supporting automatic serialization (JLD2). It is self-contained and can be used for caching or simple data storage in any application.
Implements the Capon Beamformer (minimum variance distortionless response) for directional signal processing. It is self-contained and can be used in radar, sonar, or audio applications.
Implements a matched filter for detecting known signals in noisy environments. It is self-contained and reusable in communications, radar, and sonar.
Implements the Chirp Z-Transform for evaluating the z-transform on a spiral contour. It is self-contained and useful in spectral analysis.
Provides fast 3D transformations (rotation, translation) optimized for point cloud processing. It is self-contained and can be used in 3D graphics, robotics, or augmented reality applications.