Quadrotors are increasingly deployed in applications such as agriculture, infrastructure inspection, and maintenance. In each of these applications, the robot must navigate complex scene geometry while remaining strictly collision-free. Unlike in ground domains, even minor collisions for aerial vehicles can result in the loss of the robot. This safety requirement induces a pair of technical challenges. First, the environment must be represented with sufficient fidelity to encode complex structure, even when no ground-truth obstacle data is available. Second, a motion planner must leverage this representation to determine a collision-free path to the goal. This paper proposes a system that addresses these complementary challenges. The proposed method, AirSplan, adopts a normalized variant of 3D Gaussian Splatting that encodes high-fidelity scene geometry. It then applies a novel reachability-based motion planner that leverages the differential flatness of quadrotors to compute continuous-time collision constraints that tightly overapproximate the robot's occupancy. Experiments demonstrate that AirSplan successfully finds a path in 81.2% of challenging test cases, a significant improvement over the nearest baseline method's 51.2%.
Figures & tables
Fig. 1: AirSplan generates safe quadrotor trajectories in cluttered environments. The robot occupancy is tightly overapproximated using a reachable set (shown in pink) based on differential flatness. The scene is modeled as a Normalized 3D Gaussian Splat. A sampling-based planner jointly reasons over both representations to compute safe paths between waypoints (yellow) to the goal (red).
Fig. 2: AirSplan plans safe quadrotor trajectories through cluttered environments represented as normalized Gaussian Splats. It combines three key components. First, differential flatness is used to construct a tight sphere-based overapproximation of the robot volume along a trajectory. Second, the collision probability between each sphere and the normalized 3D Gaussian Splat is efficiently computed using a Bounding Volume Hierarchy (visualized with gray wireframe boxes). Third, a GPU-parallel sampling-based optimizer evaluates many candidate trajectories to efficiently find safe paths through dense environments.
Fig. 3: Examples of images used to reconstruct the tree environments used in planning experiments. Trees were procedurally generated using L-Systems. 3DGS models were reconstructed from these simulated observations.
Symbol
Description
Value
nσ
Gaussian culling level
10
nB
BVH buffer size
16,384
β
Risk threshold
0.01
T
Planning horizon
1s
nS
Number of trajectory samples
96
nI
Max optimizer iterations
20
TABLE I: Key planning, reachability, and collision-checking parameters used in the motion planning experiments
Fig. 4: The precision and recall of various collision-checking methods between spheres and radiance fields are evaluated with multiple risk parameters; outlined entries indicate the values used by each method during planning. The proposed collision check yields results that are equivalent to or better than the baselines, with significantly lower computational complexity.
Method
Mean Time
Std. Dev.
Splanning
122.1 ms
89.1 ms
Ours
42.2 ms
4.9 ms
TABLE II: Mean and standard deviation of time required to evaluate the collision constraint.
Method
% Success
% Stuck
% Collide
SPL
Splat-Nav
51.2%
48.8%
0 %
0.47
CATNIPS
28.8%
70.4%
0.8%
0.26
Ours
81.2%
18.8%
0%
0.66
TABLE III: Each planner ran 500 trials total, then collision-checked against the scene. SPL is success weighted by path length [ 36 ] .
Flat
PZ
Samp.
Success
Stuck
Collision
FRS
Reach.
Opt.
%
%
%
✗
✗
✗
16.2%
83.8%
0%
✗
✓
✓
49.8%
50.2%
0%
✓
✓
✗
25.2%
74.8%
0%
✓
✓
✓
81.2%
18.8%
0%
TABLE IV: Ablation study over key system components. Each row replaces proposed components with existing baselines.
Fast quadrotor flight requires safe obstacle avoidance under tight onboard compute limits. While 3D Gaussian Splatting (3DGS) provides a continuous, geometry-aware scene representation for perception-driven navigation, existing 3DGS safety filters use reduced-order models such as single- and double-integrators that ignore actuator limits and assume commanded accelerations are realized instantaneously. Building on an analytic collision cone barrier for 3DGS, we introduce a nonlinear, actuator-aware safety filter enforced through the full quadrotor dynamics. We derive a high-relative-degree collision cone exponential CBF and a backup CBF that preserves QP feasibility under input constraints using a forward-simulated backup policy. Compared with a state-of-the-art 3DGS safety filter, our approach reduces trajectory jerk by 47% and runs 2.25 times faster. We validate the method in simulation and on hardware for real-time navigation in cluttered, perception-derived environments.
Tscholl Dario, Nakka Yashwanth Kumar, Gunter Brian
Obstacle avoidance is essential for safe navigation and motion planning. Recent radiance field reconstruction methods enable object detection and modeling with high fidelity, but remain too memory- and compute-intensive for on-board perception-based path planning. To address these limitations, we propose PolyMerge to convert a large, photorealistic 3D Gaussian Splatting (3DGS) model of a scene into a lightweight representation of convex polytopes whose union provably over-approximates all obstacles in the original 3DGS model. PolyMerge tunes the polytope count to trade off conservativeness and compute cost, and integrates with control barrier functions (CBFs) to plan collision-free paths. We showcase PolyMerge in simulation and hardware experiments on a Crazyflie drone, which uses PolyMerge to compute and follow safe trajectories in real time under severe onboard compute constraints, outperforming baselines in speed while guaranteeing safety. For our code and videos, visit https://athlon76.github.io/PolyMerge-website/.
Jihoon Hong, Chih-Yuan Chiu, Sara Fridovich-Keil +1
Incorporating dense visual information into motion planning remains challenging, as geometric planners rely on abstracted scene representations that discard visual richness, while learned visual models often lack geometric interpretability and computational efficiency. This paper introduces CollisionSplatting, a simple, modular, GPU-accelerated, probability-inspired distance metric with tunable conservatism that operates directly on standard 3D Gaussian Splatting (3DGS) scenes. When combined with learned image-conditioned reward functions, this metric enables joint geometric and visual planning by unifying collision-aware costs with image-space objectives. We integrate the metric into GPU-accelerated Model Predictive Path Integral (MPPI) and Rapidly-Exploring Random Tree (RRT) planners, and show on-par or better collision-classification performance compared to representative baselines while achieving substantially higher collision-checking throughput and significantly lower VRAM usage. Finally, we demonstrate the effectiveness of our metric in real-world vision-guided navigation and manipulation tasks, highlighting 3DGS as a practical bridge between rich perception and real-time motion planning.
R. Khorrambakht, Joaquim Ortiz-Haro, Stephan Weiss +1
Machines in Motion Lab, Tandon School of Engineering, New York University · Department of Smart Systems Technologies, University of Klagenfurt, Austria. · Artificial and Natural Intelligence Toulouse Institute (ANITI)