Industrial Deployment | Chang Nie

Autonomous Valet Parking: Production-Grade Full-Stack System for Underground Garages

Mon, 01 Jan 2024 00:00:00 +0000

Project context

Voyager Tech Mass-Production Partnership

Overcoming challenges in multi-floor underground garages—including featureless corridors, dynamic obstacles, tight clearance, and total GNSS denial—this system established an onboard-compute-powered L4 autonomous valet parking stack validated across extensive real-vehicle fleet trials.

Core Deliverables & Real-World Validation: Automotive pure-onboard AVP system; 500+ in-vehicle road tests (95%+ parking success), GNSS-denied multi-sensor localization, narrow-slot maneuvering, and remote summoning

My contributions

As project lead, architected the full-stack AVP system framework; directed planning and control under GNSS denial, completing 500+ in-vehicle road tests with 95%+ parking success.

Automated Valet Parking (AVP) encompasses autonomous stall search, tight-space bay parking, and designated-point summoning. In contrast to infrastructure-heavy vehicle-to-infrastructure (V2I) approaches—which require deploying dense ultra-wideband (UWB) anchors, roadside LiDARs, or dedicated camera networks throughout parking structures—vehicle-centric AVP requires zero structural modifications to parking facilities. By relying entirely on onboard sensors and compute, it delivers superior cross-facility generalization and commercial scalability.

In partnership with Tier-1 smart driving supplier Voyager Tech, we developed and deployed an onboard AVP system utilizing automotive-grade mass-production sensors: 4 surround-view ultra-wide fisheye cameras and 12 ultrasonic sensors. As Project Tech Lead, I directed the top-level system architecture and led algorithm development across the integrated driving-parking planning and control stack, validating GNSS-denied multi-sensor SLAM, corridor dynamic obstacle avoidance, Hybrid A* tight-space maneuvering, and low-speed chassis control on real production test vehicles.

Key Engineering Bottlenecks in Underground Garages

Underground garages represent semi-enclosed, highly constrained operational design domains (ODDs) that present formidable physical and geometric challenges to autonomous systems:

Total Satellite Denial & Severe Perceptual Degeneracy (GNSS-Denied & Perceptual Aliasing): Upon entering a basement structure, satellite positioning is immediately lost. Simultaneously, multi-story garages present uniform, highly repetitive architectural features: mirrored structural pillars, polished epoxy resin flooring with severe specular reflections, and featureless concrete walls. Under dim, flickering fluorescent illumination, standard point-feature extractors (such as ORB or SIFT) suffer from catastrophic false associations, precipitating visual odometry drift and scale collapse.
Severe Geometric Confinement & Non-Holonomic Kinematic Limits: Primary basement aisle widths typically measure between 2.8 and 3.2 meters, with clearance between concrete pillars occasionally narrowing below 2.5 meters. Standard passenger vehicles feature wheelbases approaching 3 meters and overall lengths near 5 meters. Constrained by non-holonomic Ackermann steering kinematics, the vehicle cannot translate laterally. When executing reverse maneuvers into a standard 2.4m $\times$ 5.2m perpendicular bay, the vehicle must execute multi-point forward-reverse (D/R gear shift) maneuvers under safety margins of less than 30 cm, where minor tracking deviations cause structural collisions.
Dynamical & Control Dichotomy Between Cruising and Parking: During corridor cruising, the system requires continuous, smooth motion at 10–15 km/h, with lateral acceleration strictly constrained for passenger comfort. Conversely, during stall insertion, vehicle velocity drops below 3 km/h, while steering actuators must rapidly sweep toward maximum lock within minimal linear displacement. This induces massive static tire scrub torque against the floor. Conventional trajectory-tracking controllers fail under this dual-regime operation, triggering actuator oscillation, severe deadband lag, or tracking overshoot.
Multi-Story Spiral Ramps & Dynamic Grade Distortions: Underground garages incorporate steep connecting spiral ramps with grades ranging between 10% and 15% (5.7°–8.5°). During ramp traversal, dynamic suspension compression induces pronounced chassis pitch and roll angles, introducing geometric skew into inverse perspective mapping (IPM). Furthermore, the longitudinal gravitational component severely perturbs velocity feedback loops, risking roll-back during stop-and-go maneuvers on inclines.

To systematically address diverse user requirements, the system supports four operational workflows:

Prior Map Condition	Mission Mode	System Execution	Target Outcome
Mapped Garage	Memorized Slot	Load local semantic graph and localize	Direct cruise to user’s assigned stall
Mapped Garage	Random Search	Cruise along aisles and detect open spaces	Immediate trajectory planning into first valid slot
Unmapped Facility	Exploratory Cruise	Build topological slot map on the fly	Explore garage, identify valid spot, and park
Mapped Garage	Remote Summon	Receive app command and execute park-out	Navigate through aisles to pickup foyer

System architecture

Campus-scale automated valet parking

From user intent and onboard sensing to mapping, hierarchical planning, vehicle control, and safety replanning.

Task, sensing, and compute

01 · HMIMaps and parking tasksMap download/update · park-in/out · remote summon

02 · SensorsSurround and near-field sensingFour fisheye cameras · ultrasonic sensing · IMU · wheel/GPS

03 · VehicleOnboard compute and chassisEmbedded GPU/ECU · CAN · steering, braking, and drive

Autonomy core

04 · PerceptionBEV multi-task perceptionSlots, markings, and text · drivable space · obstacle detection and tracking

05 · LocalizationLocalization and semantic mapVisual/inertial/wheel fusion · loop closure · semantic ICP · exploration mapping

06 · PlanningHierarchical driving and parking planningGlobal guidance · local avoidance · Hybrid A* · optimal-control smoothing

Program leadership and integrationSystem design, module interfaces, and vehicle integrationArchitecture and milestones · cross-module interfaces · planning/control focus · scenario testing and fault correction

Execution, safety, and application

07 · ControlLateral/longitudinal controlTrack the reference trajectory and command the vehicle

08 · SafetyObstacle response and replanningNear-field coverage · dynamic updates · stop and detour

09 · ServiceExplore, park, retrieve, and summonKnown/unknown maps · random, remembered, or assigned slots · return to pickup

Full-Stack Architecture & Algorithmic Design

To overcome these constraints under onboard embedded compute limits, we developed a tightly coupled, modular processing pipeline encompassing bird’s-eye-view multi-task perception, factor-graph fusion localization, hierarchical planning, and precision chassis actuation.

1. Surround-View Fisheye BEV Multi-Task Perception & Near-Field Shielding

The sensing suite consists of 4 automotive-grade ultra-wide fisheye cameras (each offering $>190^\circ$ horizontal FOV) positioned at the front, rear, and wing mirrors, paired with 12 ultrasonic transceivers.

Dynamic Pitch-Compensated Inverse Perspective Mapping: To eliminate image warping caused by vehicle acceleration, braking, and ramp slope variations, high-frequency IMU pitch and roll estimates are fed directly into the online homography transformation. This dynamic transformation stabilizes the bird’s-eye view, ensuring projected ground markings and stall boundaries maintain rigorous metric scale.
Lightweight Shared Backbone with Multi-Task Branched Heads: Operating within embedded thermal envelopes, the perception network adopts a single feature-extractor backbone paired with four decoupled task heads: parking stall head, ground marking head, obstacle detection head, and free space boundary head.
Ultrasonic Near-Field Time-of-Flight Protection: When approaching obstacles within close range, vehicle blind zones fall outside camera sightlines. Short-range and long-range ultrasonic sensors establish a continuous proximity barrier, using multi-transceiver time-of-flight triangulation to filter multi-path false echoes and provide deterministic hardware-level collision prevention.

2. Factor-Graph Multi-Sensor Fusion Odometry & Hierarchical Mapping

To guarantee drift-free localization across extended GNSS-denied trajectories, we constructed a tightly coupled factor-graph optimization backend:

Slip-Adaptive Wheel Odometry & IMU Preintegration: On polished epoxy basement surfaces, tire micro-slip frequently occurs during cornering and braking. The fusion filter continuously evaluates consistency between non-driven wheel speeds and IMU yaw rates, adaptively tuning observation covariances to bridge disturbances without pose divergence;
Visual-Semantic Landmark Constraints & Global Loop Closure: In addition to IMU and odometry factors, the factor graph integrates geometric constraints derived from detected parking slot corners and lane centerlines, triggering full pose-graph relaxation upon loop detection to eliminate accumulated drift;
Hierarchical Lightweight Map Representation: A local rolling occupancy grid caches dynamic obstacles within a sliding window, while a global topological semantic graph stores corridor centerlines and stall attributes with minimal memory footprints.

3. Hierarchical Cruising & Parking Motion Planning

Recognizing the distinct kinematic scales of aisle navigation versus stall maneuvering, the planning pipeline is decoupled into two specialized stages:

01Task and map state Known/unknown map · target slot or pickup point · current pose

02Global driving guide Centerline graph · improved A* · turn-radius arcs · B-spline

03Local avoidance trajectories State sampling · centerline/transition/obstacle/curvature costs · online replanning

04Parking search Orientation-aware Hybrid A* · nonholonomic vehicle constraints · collision checks

05Trajectory optimization and execution Optimal control · IPOPT · lateral/longitudinal tracking · feedback loop

Corridor Cruising: Reference Spline Smoothing & Dynamic Evasion

In aisle transit, the global planner fits continuous-curvature reference corridors using spline curves. The local planner samples polynomial trajectory candidates in the Frenet frame at high frequency, evaluating safety distance, comfort, and lateral acceleration to smoothly evade obstacles or yield to traffic.

Bay Parking: Modified Hybrid A* & Non-Linear Optimal Control

Upon reaching the designated stall entry position, the system transitions to precision parking mode using a two-stage coarse-to-fine framework:

Coarse Geometric Search (Modified Hybrid A)*: Search progresses in continuous configuration space under non-holonomic turning radius constraints and distance field heuristics, generating kinematically feasible initial paths within milliseconds;
Continuous Trajectory Optimization: Taking the Hybrid A* path as an initial guess, constrained non-linear optimal control enforces kinematic differential equations, steering rate limits, and multi-circle collision avoidance, producing continuous, curvature-smooth trajectories that prevent driveline shudder.

4. Precision Chassis Tracking & Delay Compensation

At low crawl speeds with aggressive steering angles, drive-by-wire latency constitutes the primary source of path deviation:

Kinematic Preview with Curvature Feedforward: Lateral control employs delay-adaptive preview lookahead, combining curvature feedforward with tracking error feedback to compensate for steering actuator delay;
Grade-Adaptive Gravitational Feedforward: Longitudinal tracking couples speed feedback with real-time IMU grade estimation, directly compensating for gravity components on steep ramps to prevent roll-back;
Multi-Tier Hardware Safety Watchdog: High-frequency watchdogs monitor system communications, issuing direct emergency brake requests if ultrasonic sensors detect unexpected close-proximity incursions.

Real-Vehicle Field Verification

Surround-View BEV Multi-Task PerceptionDynamic in-vehicle playback: real-time bird-eye view semantics, 4-way unwrapped fisheye streams, and high-precision parking space and lane marking tracking.

Autonomous Garage Exploration & Full-Stack System IntegrationAutonomous vehicle operation in multi-floor garages: synchronized surround perception, visual odometry, vehicle kinematics, and incremental semantic parking map construction.

Field trials were conducted across diverse commercial underground parking facilities, spanning modern multi-story garages, unlit legacy basements, and complex spiral structures:

Perpendicular, Parallel, and Slanted Bay Ingress: In tight stalls flanked by parked vehicles with less than 30 cm of lateral margin, the vehicle consistently parked within two to three forward-reverse maneuvers, achieving alignment within 1° of stall markings and symmetric wheel clearance;
Multi-Floor Spiral Navigation & Dynamic Clearance: Across hundreds of meters of subterranean transit, the vehicle smoothly negotiated consecutive blind corners, executing automatic lane yields when encountering oncoming delivery vans or wandering pedestrians;
End-to-End Remote Summoning: Production vehicles operated completely unmanned in response to mobile app summon commands, autonomously exiting bays and arriving at ground-level passenger staging areas.

Engineering Takeaways & Architectural Lessons

Automated Valet Parking represents one of the most demanding low-speed autonomous driving domains due to tight geometric tolerances and extreme perceptual degradation. Key takeaways include:

Multi-Modal Synergy in Featureless Environments: When individual sensing modalities degrade, combining vehicle kinematics, IMU preintegration, and topological semantic landmarks provides the only viable defense against long-term localization drift.
Explicit Physics Constraints in Trajectory Formulation: Kinematic paths must not be treated as purely geometric splines. Embedding actuator rate limits, acceleration limits, and gear-shift costs directly into the optimization formulation is essential for smooth, production-grade tracking.
Decoupled Architecture with Deterministic Safety Shields: Decoupling open corridor cruising from tight stall optimization preserves high real-time control frequencies, while dedicated hardware-level ultrasonic emergency braking guarantees operational safety under real-world uncertainties.

Commercial Autonomous Mowing Robot: Multi-Modal Fusion & Terrain-Aware 3D Perception

Sun, 01 Jan 2023 00:00:00 +0000

Project context

Positec Group (WORX) Mass-Production Partnership

Overcoming the severe challenges of unstructured, deformable outdoor turf—including thick grass returns, non-planar slopes, hidden trenches, and dynamic pedestrian/pet traffic—this system brings automotive-grade autonomous driving technologies to outdoor mobile robotics, pioneering truly scene-aware, wire-free lawn robotics.

Key Deliverables & Field Validation: Commercial-grade integrated robotic hardware-software system; >98% obstacle avoidance success rate, terrain-adaptive 3D obstacle perception engine, positive/negative obstacle extraction, and field-proven autonomous mowing

My contributions

As project lead, developed slope-adaptive LiDAR point-cloud filtering and visual obstacle avoidance for Positec robotic lawn mowers, achieving >98% obstacle avoidance success rate and resolving point-cloud clutter filtering, positive/negative obstacle separation, and multimodal sensor fusion on rolling terrain.

Commercial and residential robotic mowers have traditionally relied on low-frequency inductive perimeter wires buried beneath turf edges, coupled with random-bounce navigation. Over expansive undulating estates, municipal parks, or irregular municipal greens, physical perimeter wiring incurs prohibitive civil trenching costs, difficult wire fault troubleshooting, and inherent random-walk inefficiencies characterized by high re-mow overlap and missed cutting patches. Boundary-wire-free autonomous perception, all-terrain topological navigation, and systematic parallel pattern mowing represent the defining technological paradigm for next-generation outdoor groundskeeping robotics.

This project partnered with global outdoor power equipment leader Positec Group (WORX) to engineer a production-ready, boundary-wire-free commercial mowing robot equipped with full-scene autonomous perception, self-adaptive mapping, and global coverage motion planning. As Project Tech Lead, I directed the top-level full-stack hardware-software architecture, cross-module subsystem integration, and field deployment; simultaneously, as principal perception lead, I directed algorithm development for multi-beam LiDAR terrain-adaptive point-cloud modeling, bifurcated positive/negative obstacle extraction, and spatial cross-modal vision-LiDAR fusion.

Key Perception Challenges in Undulating Turf

On structured urban asphalt, the ground surface is predominantly rigid, planar, and continuous; point cloud reflections protruding 10 cm above the surface unambiguously signify physical obstacles. However, when transitioning into unstructured, undulating outdoor turf, these foundational geometric assumptions break down completely:

Vegetation Clutter & Multiple-Scattering Ambiguity (“Grass vs. Obstacle”): Turfgrass blades are flexible, semi-translucent, non-rigid, and continuously wind-perturbed. Infrared laser pulses penetrating dense turf experience multiple diffuse scattering across grass tips, mid-stems, and underlying soil. In raw 3D point clouds, this manifests as a loose, porous noise blanket 20 to 35 cm thick. If points protruding above bare soil are treated as obstacles, the mower becomes paralyzed by false phantom obstacles; conversely, applying naive 30 cm geometric height filtering causes low garden hoses, tree roots, perimeter curbs, and recumbent domestic pets to be completely overlooked, risking blade damage and safety hazards.
Non-Planar Undulating Terrain & Local Tangent Variations: Real outdoor lawns feature rolling hills, swales, drainage mounds, and subtle undulating contours. Global planar ground extraction (such as standard RANSAC ground fitting) fails within 3 to 5 meters of the vehicle, mistaking normal uphill grass surfaces for solid vertical walls and triggering emergency halts.
The “Negative Obstacle” Crisis (Depressions, Drainage Ditches, and Step Drops): Commercial mowers feature compact chassis with drive wheel diameters of only 15 to 20 cm. On contiguous grass, 7 cm subsidence ruts, open drainage trenches, or lawn edges dropping off into patios will instantly trap front casters, strike cutting discs, or induce vehicle rollovers. Negative obstacles do not generate positive point-cloud reflections; rather, they present as voids of missing laser returns and ray-shadow gaps, rendering classical spatial clustering algorithms ineffective.
Severe Optical Variations in Outdoor Natural Environments: Lawn maintenance subjects camera sensors to harsh dynamic lighting transitions: unshielded midday sun, dappled tree shadows, and acute morning/evening glare. Active structured-light infrared sensors suffer complete pattern washout under direct solar irradiance, while passive stereo algorithms experience extensive disparity dropouts across repetitive, textureless green turf.
Ultra-Low Embedded Power & Thermal Budgets: Unlike passenger autonomous testbeds carrying water-cooled industrial PCs in vehicle trunks, autonomous mowers operate strictly on compact onboard lithium battery packs. The complete SLAM, perception, and coverage planning pipeline must execute deterministically at 20 Hz within a compute budget limited to ~15 Watts on embedded edge SoCs (such as NVIDIA Jetson modules).

System architecture

An autonomous mobile robot for unstructured lawns

Localization, perception, planning, and control share one onboard sensor and compute platform.

Onboard sensing and compute

01 · VisionMulti-camera and RGB-DLawn, objects, semantics, and appearance

02 · GeometryLiDARRange, terrain, and obstacle point clouds

03 · MotionRTK/GNSS · IMU · wheel odometryGlobal constraints and continuous local motion

Autonomy core

04Multi-sensor localizationVision, LiDAR, inertial, wheel, and satellite cues

05Visual objects and semanticsMowable area, object categories, and scene semantics

06 · Program leadership and technical focusSystem integration + LiDAR obstacle detection + multi-sensor obstacle fusionLead module integration and field commissioning; separate obstacles from uneven grass and unify 3D geometry, semantic segmentation, and visual detections for planning

Robot behavior

07Local environment modelTraversable lawn and dynamic/static obstacles

08Path and coverage planningSafe avoidance, coverage mowing, and return-to-charge

09Chassis and mowing controlTurn perception into real-time motion decisions

Core Algorithmic Architecture & Engineering Design

To achieve reliable autonomy under low-power compute and natural outdoor environmental degradation, we developed a full-stack autonomy pipeline spanning factor-graph SLAM, polar terrain manifold modeling, positive/negative obstacle decoupling, and cross-modal sensor fusion.

1. Factor-Graph Multi-Sensor Fusion Localization & Slip Adaptation

Maintaining tightly spaced, millimeter-parallel “boustrophedon” (lawn-striping) patterns over hours of continuous operation demands high-rate, drift-free 6-DOF pose estimation:

Heterogeneous Sensor Topology: The platform integrates dual-antenna RTK-GNSS, an automotive-grade 6-axis IMU, precision wheel encoders, and a multi-beam 3D LiDAR. In open sky, dual-antenna differential GNSS delivers centimeter-level position fixes and true orientation heading;
Slip-Adaptive Wheel Odometry Factor: On dew-soaked morning grass or damp sloping soil, mower wheels frequently encounter micro-slip and wheelspin. The system continuously cross-examines wheel acceleration against axial IMU accelerations, establishing a slip-residual monitoring mechanism that dynamically downweights wheel kinematic constraints to prevent spurious odometry drift;
Unified Smoothing Backend via GTSAM: Implemented in GTSAM, the sliding-window optimization backend simultaneously resolves RTK position factors, IMU pre-integration factors, slip-adaptive kinematic odometry factors, and LiDAR planar surface constraints over the trajectory. When transitioning under dense tree canopies or adjacent to tall buildings where GNSS signals degrade, inertial and LiDAR odometry sustain continuous localization without trajectory jumps.

2. Terrain-Adaptive LiDAR Point-Cloud Modeling & Decoupled Hazard Extraction

Extracting physical hazards out of hundreds of thousands of diffuse grass returns forms the central algorithmic breakthrough of the perception pipeline:

Terrain-adaptive point-cloud pipeline: from polar grid partitioning and slope continuity estimation to clustering, temporal tracking, and obstacle dispatch.Open full size

We built a local polar manifold terrain analysis and ray-gap detection pipeline:

Structured Polar Elevation Grid: Raw 3D point clouds within a 10-meter operational radius are projected into an egocentric polar coordinate grid. Dividing space into fine radial range cells and azimuth sectors, the system computes lowest elevation, highest elevation, point count density, and elevation variance per cell;
Radial Tangent Ground Manifold Propagation: Bypassing global ground plane assumptions, local tangent planes are propagated outward along each azimuth ray starting from the tire-ground contact footprint. Enforcing smoothness and bounded gradient step constraints, the algorithm conforms to undulating terrain contours while dynamically isolating soil levels from overlying turfgrass;
Dual-Mode Positive Hazard Classification (Protruding Obstacles): Operating across manicured lawns and wild overgrown grass:
1. Manicured Turf Mode: Point clusters exceeding 16 cm in elevation with surface normals diverging from local ground normals are classified as obstacles (tree trunks, stones, fences);
2. Overgrown Meadow Mode: Elevation thresholds adaptively elevate to 35 cm, combined with vertical point density entropy. Because grass stems produce uniformly dispersed vertical returns whereas rigid solid objects concentrate dense returns, entropy testing reliably separates dense obstacles from tall grass;
Negative Hazard Extraction (Pits, Trenches, and Cliffs): Depressions lack direct reflective returns. We model the expected line-of-sight laser propagation geometry. When encountering a ditch or step drop ($\ge 7\text{ cm}$ depth), the laser beam either passes over the depression or strikes the distant lip at an anomalous radial distance discontinuity, creating a ray-shadow gap. Detecting this discontinuity paired with abrupt elevation drops marks the negative hazard boundary, projecting a repulsive field into the local costmap;
Two-Pass Connected Component Clustering & Kalman Tracking: Surviving obstacle cells undergo fast raster clustering into 3D bounding boxes. Bounding boxes are tracked across frames via Hungarian matching and Kalman filtering, eliminating transient wind-blown grass blades that persist for only 1–2 cycles.

LiDAR delivers precise metric geometry and 3D bounding dimensions, but cannot infer object semantics (distinguishing an innocent toy from a sleeping pet). Cameras capture rich color semantics but lack reliable depth on textureless lawns.

Multi-modal obstacle fusion pipeline: aligning 3D point clusters with 2D semantic masks to output verified, category-aware navigation obstacles.Open full size

We deployed TensorRT-accelerated lightweight object detection networks on the embedded GPU, processing at 20 Hz to detect pedestrians, domestic animals (dogs, cats), hoses, garden tools, and flowerbed edges. Reprojecting 3D LiDAR clusters onto 2D camera pixels via the camera-LiDAR calibration matrix yields three complementary outputs:

Category-Aware High-Confidence Fused Targets: 3D LiDAR bounding projections match 2D visual bounding detections with high Intersection-over-Union (IoU). The target receives unambiguous category labeling (e.g., sleeping pet) and millimeter 3D coordinates. The motion planner assigns highest avoidance priority, generating a generous $>1.0\text{ m}$ standoff safety corridor;
Point-Cloud-Only Geometric Obstacles: Unclassified visual targets (e.g., untextured grey boulders, tree stumps, ground trenches) confirmed by strong LiDAR geometry. The planner handles them as rigid obstacles, adopting standard 0.3 m edge clearances;
Vision-Only Distant Precautionary Candidates: Visual detections of humans or pets at 10–15 meters where LiDAR point returns remain sparse (1–2 points). The system preemptively curtails speed, maintaining tracking focus until approaching within dense LiDAR verification range.

Field association example: visual object semantics and 3D point-cloud supports lock together in real-time navigation frames.

4. Autonomous Coverage Planning & Anti-Sideslip Control

Boustrophedon Complete Coverage Path Planning (CCPP): Decomposing complex yards into convex polygonal cells, the planner computes parallel sweeping tracks with continuous turn loops along boundaries, minimizing deadhead transit;
Gravity-Compensated Slope Anti-Sideslip Control: On lateral grass slopes reaching 20° (36% grade), gravity pulls the mower downhill, producing severe trajectory lateral slip. The wheel controller actively monitors chassis roll and pitch from the fused IMU and injects lateral velocity feedforward compensation into differential drive outputs, cancelling downhill sliding drift and preserving razor-straight cutting tracks on steep embankments.

Outdoor Field Playback

Field Playback: Terrain-Adaptive Point-Cloud PerceptionReal-world field operation: red 3D point clusters mark detected low-profile obstacles and terrain boundaries, while front/rear cameras provide real-time visual confirmation.

The field recordings above demonstrate real-world autonomous closed-loop operation on undulating villa lawns: synchronized dual-camera video feeds align with 32-beam 3D LiDAR point clouds. Red highlighted clusters cleanly reject tall grass clutter while locking onto low-profile rocks, flowerbed curbs, and terrain drop-offs under dappled sunlight and changing outdoor conditions.

Engineering Summary & Technical Takeaways

Deploying autonomous mowers into real-world unstructured greenery highlights primary engineering insights:

Leveraging Physics Priors Against Natural Ambiguity: When dealing with diffuse foliage returns and non-planar slopes, generic data-driven models or flat-ground assumptions inevitably fail. Understanding laser scattering mechanics within vegetation and parameterizing radial slope continuity allows clean clutter rejection at minimal compute cost.
Negative Obstacle Detection as an Essential Safety Prerequisite: For low-clearance mobile bases, ground trenches and step drops are as hazardous as rigid vertical walls. Elevating negative hazard detection to equal architectural priority ensures safe off-road operations.
Balancing Algorithmic Sophistication with Edge Thermal Limits: In battery-powered commercial robotics, algorithms detached from hardware constraints cannot reach production. Orchestrating lightweight neural networks with compact geometric point-cloud processing enables robust 20 Hz real-time loops on compact low-power SoCs.
Active Gravity Feedforward Beats Passive Integral Trimming: On steep, slippery outdoor grass, passive integral controllers take seconds to wind up, resulting in significant downhill lane departure. Directly feeding physical gravity projections into dynamic steering compensation keeps tracking tight across slope transitions.

Industrial Deployment | Chang Nie

Autonomous Valet Parking: Production-Grade Full-Stack System for Underground Garages

Project context

My contributions

Key Engineering Bottlenecks in Underground Garages

Full-Stack Architecture & Algorithmic Design

1. Surround-View Fisheye BEV Multi-Task Perception & Near-Field Shielding

2. Factor-Graph Multi-Sensor Fusion Odometry & Hierarchical Mapping

3. Hierarchical Cruising & Parking Motion Planning

Corridor Cruising: Reference Spline Smoothing & Dynamic Evasion

Bay Parking: Modified Hybrid A* & Non-Linear Optimal Control

4. Precision Chassis Tracking & Delay Compensation

Real-Vehicle Field Verification

Engineering Takeaways & Architectural Lessons

Commercial Autonomous Mowing Robot: Multi-Modal Fusion & Terrain-Aware 3D Perception

Project context

My contributions

Key Perception Challenges in Undulating Turf

Core Algorithmic Architecture & Engineering Design

1. Factor-Graph Multi-Sensor Fusion Localization & Slip Adaptation

2. Terrain-Adaptive LiDAR Point-Cloud Modeling & Decoupled Hazard Extraction

3. Cross-Modal Vision-LiDAR Spatial Fusion & Semantic Augmentation

4. Autonomous Coverage Planning & Anti-Sideslip Control

Outdoor Field Playback

Engineering Summary & Technical Takeaways