J-MOD$^{2}$: Joint Monocular Obstacle Detection and Depth Estimation
This work addresses obstacle avoidance for MAVs, offering a more efficient alternative to complex SLAM systems, though it is incremental as it builds on existing multi-task architectures.
The authors tackled the problem of obstacle detection and depth estimation for micro aerial vehicles (MAVs) by proposing J-MOD², an end-to-end deep architecture that jointly learns these tasks without requiring full 3D maps, and demonstrated its effectiveness in experiments with different scenarios and integration into a simulated navigation system.
In this work, we propose an end-to-end deep architecture that jointly learns to detect obstacles and estimate their depth for MAV flight applications. Most of the existing approaches either rely on Visual SLAM systems or on depth estimation models to build 3D maps and detect obstacles. However, for the task of avoiding obstacles this level of complexity is not required. Recent works have proposed multi task architectures to both perform scene understanding and depth estimation. We follow their track and propose a specific architecture to jointly estimate depth and obstacles, without the need to compute a global map, but maintaining compatibility with a global SLAM system if needed. The network architecture is devised to exploit the joint information of the obstacle detection task, that produces more reliable bounding boxes, with the depth estimation one, increasing the robustness of both to scenario changes. We call this architecture J-MOD$^{2}$. We test the effectiveness of our approach with experiments on sequences with different appearance and focal lengths and compare it to SotA multi task methods that jointly perform semantic segmentation and depth estimation. In addition, we show the integration in a full system using a set of simulated navigation experiments where a MAV explores an unknown scenario and plans safe trajectories by using our detection model.