MonoNav*(right) collides because of erroneous 3D reconstruction and a simplistic planning approach whereas ours(left) successfully navigates to the goal position whilst avoiding obstacles.
*MonoNav: MAV Navigation via Monocular Depth Estimation and Reconstruction, Symposium on Experimental Robotics (ISER)
Authors: Nathaniel Simon and Anirudha Majumdar
Intelligent Robot Motion Lab, Princeton University
While NoMaD(right)-predicted trajectories tend to steer away from the obstacle, they lack sufficient precision, resulting in a collision whereas our method(left) explicitly incorporates depth uncertainty into risk-aware trajectory optimization, enabling successful navigation even in complex scenes.
CARE*(Right) applies collision correction on NoMaD trajectories
Noise in the estimated depth/pcd results in erroneous correction and leads to collision in narrow spaces
*CARE: Enhancing Safety of Visual Navigation through Collision Avoidance via Repulsive Estimation,
Kim, Joonkyung and Sim, Joonyeol and Kim, Woojun and Sycara, Katia and Nam, Changjoo
9th Annual Conference on Robot Learning (CoRL), 2025
ROS Navigation Stack(ROSNAV)(right) uses global planning followed by Dynamic Window Approach (DWA) for local control on costmaps built from estimated point clouds. This classical approach fails mainly due to its inability to handle noise in the estimated depth. Offsets between estimated and true point clouds lead to false free space and collisions(top right), while noisy estimates inflate costmaps near goals, causing planning failures(bottom right).