Detecting abnormal system states and taking corrective action help ensure stable system performance and a minimum of deviation from expected behavior. To this end, Isaac SDK features a monitoring framework that can be equipped with a variety of system observation components. This framework currently contains components specific to monitoring the current state of a robot’s localization. With its modular approach, it can be customized easily for individual scenarios and use cases. It consists of multiple components that work in tandem:
- A central
Monitorcomponent that collects system performance grades from individual evaluator components.
- Evaluator components that regularly send performance grades about specific parts of the system
to one or more connected
Monitorcomponents. The following evaluators are part of this release:
flatscan_localization::MonitorEvaluatorFrequency: Observes the frequency with which messages are received on a specified
FlatscanProtochannel and reports deviations from an expected frequency to the
flatscan_localization::MonitorEvaluatorMapscan: Continuously compares received
FlatscanProtolaser scan messages with predicted laser scans for the currently estimated robot pose. Reports the degree of agreement to the
flatscan_localization::MonitorEvaluatorSvio: Observes the drift between two different pose estimation frames and reports the deviation between both over time. This can be used, for example, to detect drift between a laser-scan based localization and a Stereo Visual Inertial Odometry based localization.
EvaluatorGradeFusioncomponent that is used by the
Monitorto combine multiple received performance grades to form an overall grade that can be used for deriving action. In this release, one type of
MaxValueGradeFusion: Returns the highest (worst) grade value of all currently received evaluator grades.
Monitor instances can be present in the same system, and evaluators can report
their data to multiple
Monitor instances in parallel by connecting their
to any unique input channel of a
Monitor instance. The following parameters are available in
evaluator_max_age: Should, at any point, any evaluator stop sending messages for more than the duration specified in this parameter (in seconds), a failure is reported.
fused_grade_threshold: When the
EvaluatorGradeFusioninstance currently used by the
Monitorinstance returns a value larger than this parameter, a failure is reported.
settle_time: The time (in seconds) to wait after starting this codelet to start evaluation. This gives the overall system time to settle after an initial ramp-up phase and prevents action based on data that is not representative of a running system state.
The general localization subgraph includes a Localization Monitor setup that makes use of the first
two evaluators. Should a failure be reported by the
Monitor instance, global localization is
restarted. The base configuration can be changed via the following configuration keys:
navigation.localization.localization_monitor.Monitor -> evaluator_max_age navigation.localization.localization_monitor.Monitor -> fused_grade_threshold navigation.localization.localization_monitor.Monitor -> settle_time navigation.localization.evaluator_frequency.Evaluator -> threshold navigation.localization.evaluator_frequency.Evaluator -> expected_frequency navigation.localization.evaluator_mapscan.Evaluator -> map navigation.localization.evaluator_mapscan.Evaluator -> range_scan_model navigation.localization.evaluator_mapscan.Evaluator -> flatscan_frames navigation.localization.evaluator_mapscan.Evaluator -> robot_frame navigation.localization.evaluator_mapscan.Evaluator -> beam_distance_threshold navigation.localization.evaluator_mapscan.Evaluator -> good_beams_threshold
Depending on the scenario the Localization Monitor is deployed in, the
parameter needs to be adapted to the actual expected frequency of the monitored flatscan
source. If in doubt, use a lower expected frequency, as a higher than expected frequency
does not negatively influence the
Monitor behavior. The
threshold parameter can be
adjusted if the amplitude of change in frequency is comparatively high to prevent spurious failure
reports from the
Should the ramp-up time of the system be more than the default value for the
settle_time parameter can be adjusted accordingly. In cases where
Monitor decisions seem too conservative, e.g. when failures are reported too early because
of very noisy sensor input, the
fused_grade_threshold can be increased to accomodate for
If the occupancy grid map used for localization changes often does not exactly represent the
actual environment, the
from 0.0 to 1.0) can be increased.
If you wish to implement additional evaluators or grade fusions, note the following design guidance:
- Evaluators must send messages of type
CompositePrototo a unique input channel of a
Monitorinstance. These messages must contain the field
grade, which should be 0.0 if the expected performance result was reached. Positive values (ideally normalized to 1.0) indicate that performance is worse than expected, and negative values (normalized to -1.0) indicate that performance is better than expected.
- Grade Fusion components must derive from the component
::isaac::monitor::EvaluatorGradeFusionand must implement its interface. The values returned by
fuseGradesshould follow the metric for evaluator grades described above.
Visualizing the current Monitor state in Isaac Sight¶
The current state of the Monitor and the evaluator components can be visualized using Isaac Sight.
This includes the overall grade of the Monitor, as well as the individual evaluator grades
and their specific values (such as the frequency measured by
In Sight, activate the following message channels to visualize the Monitor and evaluator grades (their location may vary based on the actual application setup):
navigation.localization.evaluator_frequency.Evaluator -> grade navigation.localization.evaluator_mapscap.Evaluator -> grade navigation.localization.localization_monitor.Monitor -> grade
These channels can then be visualized in a 2D plot to analyze the weak points of localization in a given scenario:
The internal states of the individual evaluators can also be visualized: