Cognigear
Autonomy Stack Engineering

Perception & Localization Stack Audit & Upgrade

Audit your detection/localization stack, identify failure modes (dust, rain, night, occlusions), and design model + data upgrades.

Timeline
6 Weeks to Value
Typical Engagement
$60k–$180k
Focus Areas
Haul trucks, LVs, Surveillance robots

Perception & Localization Stack Audit & Upgrade

Fix the "edge cases" that are stopping your autonomy pilot from graduating to production. We harden your stack against dust, rain, night, and GPS loss.

  • Diagnose why your localization jumps or drifts in specific zones
  • Reduce phantom braking by tuning perception thresholds and fusion logic
  • Architecture upgrades for GNSS-denied operation (LiDAR/Visual SLAM)

Who this is for

Autonomy Engineers, Computer Vision Leads, and Robotics Directions at:

  • Mining companies struggling with dust and weather downtime
  • Warehouse operators facing "lost robot" issues in changing aisles
  • Robotics startups needing 3rd party validation of their stack

Operational context

This engagement focuses on:

  • Failure Modes – Dust/Snow/Rain clutter, featureless corridors (long walls), dynamic occlusions
  • Technologies – NDT/ICP matching, Graph SLAM, Kalman Filtering, Deep Learning object detection (YOLO/SSD/Transformers)
  • Data – Dataset curation, annotation quality, and model re-training

Trigger phrases you might be saying

  • “The trucks stop every time the sun sets because of shadows.”
  • “We lose localization when the big haul trucks park next to us.”
  • “Our falsely detected obstacles are crushing our productivity.”
  • “We need to operate in the tunnels where there is zero GPS.”

Business outcomes

  • Increased ODD availability (e.g., operating through light dust or rain)
  • Higher safety confidence through validated perception performance
  • Reduced manual interventions caused by localization faults
  • Clear roadmap for moving from "90% works" to "99.99% works"

What we deliver

  • Root cause analysis of current failure modes (log review + site reproduction)
  • Localization architecture upgrade design (e.g., adding visual odometry)
  • Perception model re-training strategy (data curation & augmentation)
  • Evaluation dataset creation for regression testing
  • Tuning of fusion parameters (Kalman Filter covariance matrices, etc.)

How it works

  1. Audit – Deep dive into your bag files / logs to categorize failures
  2. Benchmark – Quantify current performance (Recall, Precision, Pose Error)
  3. Upgrade – Propose and prototype architectural or algorithmic fixes

Timeline & effort

  • Duration: 6-8 weeks
  • Client time: Engineering team deep dives (~4 hours/week), access to logs
  • Data: Rosbags or logs of failure scenarios, calibration data, maps

Pricing bands

Fixed-fee: $60k–$180k, depending on:

  • Complexity of the current stack (open source vs. proprietary black box)
  • Volume of data to analyze
  • Need for custom model training vs. architectural tuning

Tech stack & integrations

We work deep in the code:

  • Algorithms: LOAM, Cartographer, GICP, EKF/UKF, PointEiffel
  • Frameworks: ROS/ROS2, OpenCV, PCL, PyTorch, TensorRT
  • Mapping: HD Maps, vector maps, occupancy grids

Risks & safeguards

We explicitly design for:

  • Regression risk – ensuring updates don't break functionality that already works
  • Compute constraints – ensuring new models fit on your existing edge hardware
  • Data bias – ensuring training data covers the full diversity of your operational environment
  • Safety bounds – defining "uncertainty thresholds" where the system must stop

Site examples

  • Underground Mine (Australia) – Upgraded a localization stack to switch seamlessly between wall-following (in featureless tunnels) and scan-matching (in intersections), reducing lost-robot incidents by 95%.
  • Port Terminal (Europe) – Tuned perception filters to ignore steam and exhaust fumes from other vehicles, which were previously causing constant phantom emergency braking events.

Frequently asked questions

Can you fix our dusty LiDAR problem? We can improve the software filtering (e.g., dynamic radius outlier removal, intensity filters) and fusion logic, which often solves 80% of dust issues without changing hardware.

Do you label the data for us? We define the labeling strategy and ontology. We partner with labeling bureaus for the manual labor, or set up the pipeline for you.

Do we need to share our source code? It helps, but we can also work with "grey box" testing if interfaces and logs are sufficient. However, for "Upgrade" services, code access is usually required.

Target KPIs

  • ↑Mean Time Between Localization Failures
  • ↑Object detection recall @ range
  • ↑False positive rate (phantom braking)
  • ↑Pose estimation accuracy
  • ↑Compute load reduction

Deployed Environments

GPS-denied tunnelsDusty open-pitsDynamic yards

Ready to start?

Book a 15-minute technical scoping call to discuss your fleet requirements.

Book Scoping Call

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