nav tooling

Testing KronNav on a dataset

build kronknav

uv run maturin develop --uv --release -m dimos/mapping/ray_tracing/rust/Cargo.toml && uv run maturin develop --uv --release -m dimos/navigation/nav_3d/mls_planner/rust/Cargo.toml

run on a dataset

uv run python -m dimos.navigation.nav_3d.mls_planner.utils.plan_rrd ~/recordings/china_office_pointlio_sensor.db --robot-height 0.4 --lidar-stream pointlio_lidar --odom-stream pointlio_odom

How to Optimize Point-LIO Configs

1. Record mid360 with PCAP enabled, see the go2_mid360.md doc (in dimos)
2. Use pcap_to_db.py to generate alternative lidar/odom outcomes - renders to rerun

Pcap to DB

You can see/edit the default config here.

python -m dimos.hardware.sensors.lidar.pointlio.scripts.pcap_to_db --pcap mid360_shake_stairs/mid360_shake_stairs.pcap --db stairs.db

will open up rerun with aggregated map + odom path create a new db file with the pointlio stream

generate a global map from this via

dimos map global --lidar pointlio_lidar --pgo-tol=0 --no-carve

Options

Flag	Default	Meaning
--pcap	(required)	Livox Mid-360 pcap (a missing path is fetched via get_data)
--db	<pcap>.db	Target memory2 db. Existing → append/align; missing → built from scratch (or fetched via get_data)
--rate	1.0	Replay-speed multiplier
--odom-freq	30.0	Point-LIO odometry rate (Hz)
--max-sensor-sec	0 (whole pcap)	Stop after N sensor seconds
--warmup-sec	4.0	Seconds the fake lidar waits before streaming (lets Point-LIO come up)
--no-rrd	off	Skip writing the <db>.rrd quick-look
--voxel	0.2	Voxel size (m) for the .rrd aggregated map
--host-ip	192.168.1.5	Host IP (override to run two replays at once)
--lidar-ip	192.168.1.155	Synthetic lidar IP
--alias-iface	dimos-mid360	Dummy iface the host/lidar IPs live on
--no-network-setup	off	Don't let the module alias the NIC via sudo — you've set up the IPs + routes yourself

Tuning flags

Flag	Default	Meaning
--con-frame	off	Accumulate multiple sweeps into one frame
--con-frame-num	1	Sweeps per accumulated frame (con_frame)
--cut-frame	off	Split each sweep into time sub-frames
--cut-frame-time-interval	0.1	Sub-frame interval (s) when cut_frame
--time-lag-imu-to-lidar	0.0	IMU→lidar clock offset (s)
--lidar-type	avia	Driver branch: avia (Livox Mid-360) / velodyne / ouster / hesai / unilidar
--scan-line	4	Number of scan lines
--scan-rate	10	Scan rate (Hz)
--timestamp-unit	nanosecond	Per-point timestamp unit: second / millisecond / microsecond / nanosecond
--blind	0.5	Spherical min range (m); nearer points dropped
--point-filter-num	3	Keep every Nth raw point (1 = all)
--use-imu-as-input	off	IMU-as-input model (default robust IMU-as-output)
--prop-at-freq-of-imu	on	Propagate state at IMU frequency
--check-satu	on	Zero residuals on saturated IMU samples
--init-map-size	10	Initial iVox map size
--space-down-sample	on	Voxel-downsample each scan (leaf = --filter-size-surf)
--satu-acc	3.0	Accel saturation threshold (g)
--satu-gyro	35.0	Gyro saturation threshold (deg/s)
--acc-norm	1.0	IMU accel unit (1 = g, 9.81 = m/s²)
--plane-thr	0.1	Plane-fit residual threshold (m)
--filter-size-surf	0.2	Pre-KF scan downsample leaf (m)
--filter-size-map	0.5	Persistent map voxel leaf (m)
--ivox-grid-resolution	2.0	iVox local-map grid (m)
--ivox-nearby-type	nearby6	iVox neighbour stencil: center / nearby6 / nearby18 / nearby26
--cube-side-length	1000.0	Map cube side length (m)
--det-range	100.0	Max detection range (m)
--fov-degree	360.0	Horizontal FOV (deg)
--imu-en	on	Use the IMU
--start-in-aggressive-motion	off	Skip the static IMU-init assumption
--extrinsic-est-en	off	Online-estimate the IMU→lidar extrinsic
--imu-time-inte	0.005	IMU integration step (s)
--lidar-meas-cov	0.01	Lidar measurement covariance
--acc-cov-input	0.1	Accel process cov (input model)
--vel-cov	20.0	Velocity process covariance
--gyr-cov-input	0.01	Gyro process cov (input model)
--gyr-cov-output	1000.0	Gyro process cov (output model)
--acc-cov-output	500.0	Accel process cov (output model)
--b-gyr-cov	0.0001	Gyro-bias random-walk covariance
--b-acc-cov	0.0001	Accel-bias random-walk covariance
--imu-meas-acc-cov	0.01	Accel measurement covariance
--imu-meas-omg-cov	0.01	Gyro measurement covariance
--match-s	81.0	Point-to-plane match scale
--gravity-align	on	Align initial gravity to −Z
--gravity	0 0 -9.81	Gravity vector: x y z (m/s²)
--gravity-init	0 0 -9.81	Initial gravity estimate: x y z (m/s²)
--extrinsic-t	-0.011 -0.02329 0.04412	IMU→lidar translation: x y z (m)
--extrinsic-r	identity	IMU→lidar rotation: 9 values row-major
--publish-odometry-without-downsample	off	Publish odom per scan, no downsample
--odom-only	off	Odometry only, skip map publishing

MacOS Caveats

The module aliases the synthetic IPs onto lo0, which needs sudo. A tty-less worker can't prompt, so set up the interface by hand, then pass --no-network-setup:

sudo ifconfig lo0 alias 192.168.1.5 netmask 255.255.255.0
sudo ifconfig lo0 alias 192.168.1.155 netmask 255.255.255.0
sudo route -n add -host 224.1.1.5 -interface lo0
sudo route -n add -host 255.255.255.255 -interface lo0

python -m dimos.hardware.sensors.lidar.pointlio.scripts.pcap_to_db \
    --pcap "$PCAP" --no-network-setup