{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/5"}],"enrichment":{"capability":"evo evaluates and compares trajectory output from odometry and SLAM algorithms, supporting multiple formats (TUM, KITTI, EuRoC, ROS bags) and providing CLI tools for metrics, plotting, and export.","skillfed_tags":["robotics","slam-evaluation","trajectory-analysis"],"use_cases":["Compute absolute and relative pose error metrics to benchmark SLAM or visual odometry algorithms against ground truth.","Plot and compare multiple estimated trajectories side-by-side to visualize algorithm differences.","Export trajectory statistics and error metrics to CSV or LaTeX tables for research papers.","Process ROS bag files containing pose or odometry messages without requiring a full ROS installation.","Align and scale-adjust monocular SLAM output to ground truth before metric calculation."],"what_it_does":"evo is a Python package for evaluating and benchmarking odometry and SLAM algorithm outputs. It reads trajectory data from multiple formats\u2014TUM, KITTI, EuRoC, and ROS/ROS2 bag files\u2014and provides a suite of CLI tools and a modular library for computing metrics (absolute and relative pose error), aligning trajectories, adjusting scale, and generating plots and statistical exports. The package is designed for researchers and roboticists who need to compare algorithm performance across standardized datasets or custom experiments.\n\nThe tool combines a command-line interface (evo_ape, evo_rpe, evo_traj, evo_res, evo_config) with a Python library for custom analysis. It depends on standard scientific Python stack components\u2014numpy, scipy, pandas, matplotlib, seaborn\u2014plus rosbags for ROS bag support and pillow for image handling. Installation is straightforward via pip into a virtual environment; optional PyQt6 improves the plotting GUI, and optional packages like contextily and rerun-sdk extend visualization capabilities.","worth_installing":"Yes. evo is actively maintained, has no known vulnerabilities, low install friction, and broad Python version support (3.10\u20133.14). The GPLv3 copyleft license is a constraint only if you plan to distribute proprietary derivatives. Install it if you need to evaluate odometry or SLAM trajectories; the CLI is straightforward and the library is modular for custom analysis."},"id":"evo","links":{"html":"https://skillfed.io/packages/evo","md":"https://skillfed.io/packages/evo.md","pypi":"https://pypi.org/project/evo/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-18","license_spdx":null,"license_treatment":"copyleft","name":"evo","python_support":"supports_current","summary":"Python package for the evaluation of odometry and SLAM"},"popularity":{"monthly_downloads":251508,"position":8580,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.37.0"}