bagx 0.2.0

Post-processing analysis engine for ROS2 rosbag data

bagx

A readiness checker for recorded ROS2 bags.

bagx answers a practical question: can this bag be used for SLAM, navigation, perception, manipulation, Autoware validation, or your custom robot stack?

pip install bagx
bagx eval your_bag.db3

bagx checks topic presence, rates, gaps, sensor sync, approximate pipeline latency, workflow outcomes, and domain-specific readiness signals:

• SLAM readiness: IMU noise, GNSS quality, LiDAR↔IMU sync, and whether LIO is a bad idea
• Navigation/control readiness: odom / scan / costmap rates, goal observability, and plan → cmd_vel latency
• Workflow outcome visibility: action/service success rate, terminal failures, and top failure reasons when those topics are recorded
• Perception dataset readiness: RGB / depth / infra rates, camera calibration presence, and reusable export structure
• Manipulation readiness: joint-state rate, planner visibility, and planned_path → arm execution latency
• Custom stack support: user-defined rules on topic names/types/rates/latencies for custom messages, loaded from files or plugins
• Repeatability: manifest-driven benchmark suites you can rerun across public and private bags

What bagx answers

When a rosbag is not for SLAM, the value is usually one of these:

• "Can I trust this bag for navigation debugging?" → bagx eval checks odom, scan, costmap, plan, and control-loop timing
• "Did my Autoware recording include the sensing and vehicle topics I actually need?" → bagx verifies camera, LiDAR, GNSS, /vehicle/status/*, and whether planning/control topics are present
• "Is this RGB-D bag usable for perception work, or did I forget camera_info / depth / infra?" → bagx flags stream rates, calibration topics, and RGB-D coverage
• "Did MoveIt record just planning, or also execution?" → bagx checks joint_states, action topics, and controller activity
• "We use custom messages, can bagx still help?" → bagx eval --rules warehouse_bot or --rules rules.json adds custom domains and checks without needing message decode
• "Which of these bags should become my regression benchmark?" → bagx benchmark and bagx batch eval make that repeatable

Example: catching a real problem

$ bagx eval ouster_os0-32.db3

Overall Score: 76.6/100

Recommendations:
  ✔ IMU accel noise 0.087 m/s² — good for LIO, set imu_acc_noise_density to 0.087
  ⚠ IMU gyro noise 0.022 rad/s — consider lowering IMU integration weight
  ⚠ IMU rate 50Hz is low — 200Hz+ recommended for tightly-coupled LIO
  ⚠ LiDAR↔IMU sync delay 23ms — enable per-point deskew in SLAM

Without bagx, you'd discover these issues after hours of failed SLAM runs.

For non-SLAM bags, the same workflow answers different questions:

$ bagx eval nav2_robot.db3

Recommendations:
  Nav2 topics detected
    ✔ Odometry (/robot/odom) at 50Hz — good for Nav2
    ✔ LaserScan (/robot/scan) at 12Hz — good for costmap
    ✔ Pipeline plan → cmd_vel onset: 11ms median, 76ms P95 (3 samples)

$ bagx eval r2b_galileo2

Recommendations:
  Perception topics detected
    ✔ Depth image (/camera/realsense_splitter_node/output/depth) at 30Hz — good for RGB-D perception
    ✔ Infra stereo streams are both recorded — depth debugging is possible
    ✔ Camera calibration topics are recorded — exported perception data is reusable

Install

pip install bagx

Works without ROS2 — reads .db3 files directly via SQLite.

Commands

Command	One-liner
bagx eval bag.db3	Is this bag ready? Auto-detects SLAM / Nav2 / Autoware / MoveIt / Perception / Control
bagx compare A.db3 B.db3	Which sensor config is better?
bagx sync bag.db3 /imu /lidar	Are my sensors synchronized?
bagx anomaly bag.db3	Where did sensor quality drop?
bagx scenario bag.db3	Find GNSS-lost and high-dynamics segments
bagx export bag.db3 --ai	Export to Parquet/JSON for ML
bagx batch eval *.db3 --csv	Rank an entire dataset
bagx ask bag.db3 "question"	Ask questions via LLM
bagx benchmark suite.json	Re-run a curated benchmark suite and fail CI on regressions
bagx eval bag.db3 --rules rules.json	Apply custom topic/type/rate/latency rules for your stack
bagx rules list	List discoverable built-in and installed custom-rule plugins

Representative results

Dataset	Domain	Score	What bagx tells you
Newer College	SLAM	92	LiDAR+IMU quality is strong enough for SLAM benchmarking
Ouster OS0-32	SLAM	77	IMU is too slow and sync is weak, so deskew / sensor changes are needed
driving_20_kmh_2022_06_10-16_01_55_compressed	Autoware sensing/control	99	LiDAR packets and /vehicle/status/velocity_status are present and healthy
r2b_galileo	Multi-camera perception	90.5	Eight compressed camera streams, IMU, and chassis odom are recorded and time-aligned
r2b_whitetunnel	Multi-camera perception	91.3	Eight compressed camera streams plus IMU are recorded; bagx flags noisy accel and still validates perception coverage
r2b_galileo2	Camera perception	97.5	RGB-D + infra + camera_info are all recorded, so the bag is reusable for perception work
r2b_robotarm	Manipulation perception	96.0	RGB-D + joint_states make it suitable for robot-arm perception benchmarking
nav2-deep-final-20260324-185315	Navigation/control	100	Nav2 plan, costmap, and plan → cmd_vel loop are all observable
moveit-exec-final-20260324-185315	Motion planning/control	100	MoveIt planning and arm execution are both recorded end-to-end

Auto-detects your framework

bagx recognizes topic patterns and gives framework-specific advice:

Namespaced topics are supported too, so real bags like /robot/odom, /sensing/lidar/top/pointcloud_raw_ex, or /move_group/display_planned_path are detected without renaming topics first.

$ bagx eval nav2_robot.db3
Nav2 topics detected
  ✔ Odometry (/robot/odom) at 50Hz — good for Nav2
  ✔ LaserScan (/robot/scan) at 12Hz — good for costmap
  ✔ Global plan (/plan) recorded 3 times — planner output is visible
  ✔ NavigateToPose status (/navigate_to_pose/_action/status) recorded
  ✔ Pipeline scan → cmd_vel (full loop): 41ms median, 81ms P95
  ✔ Pipeline plan → cmd_vel onset: 11ms median, 76ms P95 (3 samples)

$ bagx eval autoware_vehicle.db3
Autoware topics detected
  ✔ LiDAR (/sensing/lidar/top/pointcloud_raw_ex) at 10Hz
  ✔ Camera (/sensing/camera/front/image_raw/compressed) at 30Hz
  ✔ GNSS (/sensing/gnss/ublox/nav_sat_fix)
  ℹ Sensing/localization-only Autoware bag — skipping planning/control checks

$ bagx eval moveit_arm.db3
MoveIt topics detected
  ✔ JointState (/joint_states) at 115Hz — good for motion planning
  ✔ MoveGroup action activity recorded on /move_action/_action/status
  ✔ Joint trajectory controller activity recorded on /panda_arm_controller/follow_joint_trajectory/_action/status
  ✔ Pipeline joint_states → planned_path: 6ms median, 6ms P95 (1 sample)
  ✔ Pipeline planned_path → arm execution: 5ms median, 5ms P95 (1 sample)

$ bagx eval r2b_galileo2
Perception topics detected
  ✔ RGB image (/camera/color/image_raw) at 15Hz — good for camera-based perception
  ✔ Depth image (/camera/realsense_splitter_node/output/depth) at 30Hz — good for RGB-D perception
  ✔ Infra stereo streams are both recorded — depth debugging is possible
  ✔ Camera calibration topics are recorded — exported perception data is reusable

$ bagx eval control_loop.db3
Planning/control topics detected
  ✔ State feedback (/base/state/odom) at 25Hz — good for closed-loop control
  ✔ Control command (/drive/cmd_vel) at 20Hz — good for control-loop observability
  ✔ Planner output (/planner/path) recorded 6 times — upstream planning is visible
  ✔ Pipeline planner → command onset: 15ms median, 15ms P95
  ✔ Action result (/mission/result): 8/8 results succeeded
  ✔ Service responses (/planner/compute_path/_service_event): 8/8 requests have responses

$ bagx rules list
warehouse_bot  builtin:warehouse_bot  Wheel odom + controller command + mission path/result checks...

$ bagx eval warehouse_bag.db3 --rules warehouse_bot
WarehouseBot custom rules matched
  ✔ Wheel odometry (/warehouse_bot/wheel_odom) at 50Hz — custom rule satisfied
  ✔ Controller command (/warehouse_bot/controller_cmd) at 25Hz — custom rule satisfied
  ✔ Mission result (/warehouse_bot/mission/result) recorded — custom rule matched
  ✔ Pipeline mission path → controller: 10ms median, 10ms P95

Dogfooding

bagx is dogfooded against:

• Autoware real bags, including the official all-sensors-bag4 dataset from the Autoware documentation datasets page
• Nav2 simulator bags captured with scripts/run_ros_dogfood.py nav2-gazebo
• MoveIt simulator bags captured with scripts/run_ros_dogfood.py moveit-demo

Recent dogfood runs:

• nav2-deep-final-20260324-185315: Overall 100.0/100, LaserScan 13Hz, plan → cmd_vel onset 11ms median
• moveit-exec-final-20260324-185315: Overall 100.0/100, JointState 115Hz, planned_path → arm execution 5ms median
• autoware_isuzu_all_sensors_bag4: real bag, Overall 72.2/100, plus a sensing-only note when planning/control topics are absent
• driving_20_kmh_2022_06_10-16_01_55_compressed: official Autoware open-data bag, Overall 99.0/100, LiDAR packet sync and /vehicle/status/velocity_status captured
• r2b_galileo: official NVIDIA open-data MCAP, Overall 90.5/100, eight compressed camera streams plus IMU and chassis odom
• r2b_whitetunnel: official NVIDIA open-data MCAP, Overall 91.3/100, eight compressed camera streams plus IMU and camera calibration coverage
• r2b_robotarm: official NVIDIA open-data MCAP, Overall 96.0/100, RGB-D + joint-state manipulation perception checks
• r2b_galileo2: official NVIDIA open-data MCAP, Overall 95.3/100, camera-only RGB-D perception checks without SLAM-specific false advice

Benchmark suites

bagx now supports manifest-driven benchmark suites so public bags can become repeatable regression tests instead of one-off dogfood runs.

The repository includes benchmarks/open_data_suite.json, which covers:

• Autoware official all-sensors-bag4
• Autoware official driving_20_kmh_2022_06_10-16_01_55_compressed
• NVIDIA official r2b_robotarm
• NVIDIA official r2b_galileo
• NVIDIA official r2b_whitetunnel
• NVIDIA official r2b_galileo2

It also includes benchmarks/non_slam_suite.json, which focuses on non-SLAM value:

• NVIDIA official r2b_galileo2 camera-only perception
• NVIDIA official r2b_whitetunnel multi-camera perception
• NVIDIA official r2b_robotarm manipulation perception
• Local Nav2 dogfood bags under .cache/dogfood/
• Local MoveIt dogfood bags under .cache/dogfood/

Typical usage:

export BAGX_REALBAGS=/tmp/bagx_realbags
bagx benchmark benchmarks/open_data_suite.json
bagx benchmark benchmarks/open_data_suite.json --json benchmark-report.json
bagx benchmark benchmarks/non_slam_suite.json
bagx rules list
bagx eval my_custom_stack.db3 --rules warehouse_bot

JSON outputs now include schema_version, report_type, bagx_version, and structured findings, so they are easier to gate in CI and compare across releases without depending on human-facing recommendation text.

Typical local workflow:

# Assumes the ROS overlay is already installed at .cache/ros_overlay_nav2_test
python3 scripts/run_ros_dogfood.py nav2-gazebo \
  --duration 40 \
  --record-dir .cache/dogfood/nav2-deep-final
bagx eval .cache/dogfood/nav2-deep-final

python3 scripts/run_ros_dogfood.py moveit-demo \
  --duration 40 \
  --record-dir .cache/dogfood/moveit-exec-final
bagx eval .cache/dogfood/moveit-exec-final

Links

• Documentation
• PyPI
• Changelog