SLAM Engineer

SLAM Engineer

Full-Time | Core Team

LAT Aerospace | Perception & Autonomy

At LAT Aerospace, we’re building India’s first clean-sheet hybrid-electric STOL aircraft and a next-generation autonomy stack powering aircraft for complex missions. Our team is kicking off a new chapter — developing the full flight-control, obstacle-avoidance, GNSS-denied navigation, and swarm-coordination layers that will define LAT’s autonomy architecture. The SLAM layer is the backbone of that mission — enabling precise real-time localization, mapping, and relative positioning for single-vehicle and swarm scenarios.

What you’ll do?

• Build real-time SLAM and localization pipelines for UAVs operating in fully or partially GPS-denied environments.
• Fuse multiple perception streams — camera (mono/stereo), IMU, lidar, depth sensors, altimeters, and terrain databases — into consistent state estimates.
• Develop relative-position estimation for multi-agent systems: identifying, tracking, and localizing other drones in a swarm using visual/lidar cues.
• Integrate terrain-relative navigation (TRN) and map-matching algorithms to achieve sub-meter localization accuracy.
• Design robust VIO/VSLAM architectures that work in low-light, motion-blur, and feature-poor environments.
• Implement and optimize EKF/UKF-based sensor fusion or factor-graph methods for real-time UAV operation.
• Build and maintain simulation and evaluation pipelines for validating localization accuracy under varied conditions (urban, rural, low-altitude, high-dynamics).
• Iterate through real-world flight testing to refine robustness, latency, and compute performance on embedded hardware.

What we're looking for?

• Strong background in SLAM, VIO, VSLAM, or multi-sensor fusion for robotics or UAVs.
• Proficiency with C++ and/or Python for real-time SLAM and computational geometry.
• Hands-on experience with visual SLAM frameworks (ORB-SLAM, VINS-Fusion, OKVIS, ROVIO, Kimera, etc.).
• Expertise in state estimation using EKF/UKF, factor graphs (GTSAM, Ceres), or graph-based optimization.
• Experience working with camera + IMU fusion, calibration, synchronization, and noise modelling.
• Familiarity with lidar processing, scan matching, ICP/NDT, or lidar-inertial fusion pipelines.
• Understanding of 3D geometry, reprojection, depth estimation, and perception for robotics.
• Knowledge of embedded compute constraints and techniques for optimizing algorithms for real-time deployment.

Why LAT?

• Core-team role building the autonomy brain of India’s most ambitious aerospace startup.
• Ownership, speed, and the opportunity to define a new class of systems from scratch.