Structure-from-Motion (SfM), a task aiming at jointly recovering camera poses and 3D geometry of a scene given a set of images, remains a hard problem with still many open challenges despite decades of significant progress. The traditional solution for SfM consists of a complex pipeline of minimal solvers which tends to propagate errors and fails when images do not sufficiently overlap, have too little motion, etc. Recent methods have attempted to revisit this paradigm, but we empirically show that they fall short of fixing these core issues. In this paper, we propose instead to build upon a recently released foundation model for 3D vision that can robustly produce local 3D reconstructions and accurate matches. We introduce a low-memory approach to accurately align these local reconstructions in a global coordinate system. We further show that such foundation models can serve as efficient image retrievers without any overhead, reducing the overall complexity from quadratic to linear. Overall, our novel SfM pipeline is simple, scalable, fast and truly unconstrained, i.e. it can handle any collection of images, ordered or not. Extensive experiments on multiple benchmarks show that our method provides steady performance across diverse settings, especially outperforming existing methods in small- and medium-scale settings.

Reconstructing scenes and tracking motion are two sides of the same coin. Tracking points allow for geometric reconstruction [13], while geometric reconstruction of (dynamic) scenes allows for 3D tracking of points over time [22], [36]. The latter was recently also exploited for 2D point tracking to overcome occlusion ambiguities by lifting tracking directly into 3D [35]. However, above approaches either require offline processing or multi-view camera setups both unrealistic for real-world applications like robot navigation or mixed reality. We target the challenge of online 2D and 3D point tracking from unposed monocular camera input introducing Dynamic Online Monocular Reconstruction (DynOMo). We leverage 3D Gaussian splatting to reconstruct dynamic scenes in an online fashion. Our approach extends 3D Gaussians to capture new content and object motions while estimating camera movements from a single RGB frame. DynOMo stands out by enabling emergence of point trajectories through robust image feature reconstruction and a novel similarity-enhanced regularization term, without requiring any correspondence-level supervision. It sets the first baseline for online point tracking with monocular unposed cameras, achieving performance on par with existing methods. We aim to inspire the community to advance online point tracking and reconstruction, expanding the applicability to diverse real-world scenarios.

Tiny robots such as nano quadcopters or micro rovers are highly beneficial for various applications as they are inexpensive, agile, and safe for humans. However, their extreme size, weight, and power (SWAP) constraints lead to extremely limited compute, making autonomous navigation challenging. Existing approaches have enabled navigation within these tight constraints, but struggle in dynamic and cluttered scenes.To this end, we present Focus Bug, a novel and robust mapless navigation algorithm that can run on extremely limited hardware. Focus Bug reduces the amount of processed sensory data using a tiny reinforcement learning policy, only processing the inputs necessary for navigation. We use deep reinforcement learning (DRL) to identify critical parts of the robot’s range data and combine it with classical mapless navigation methods to benefit from their robustness and established performance. We implement and evaluate Focus Bug both on a drone in simulation and a micro-rover in the real world to show it can be applied across embodiments. Our hybrid approach outperforms the state-of-the-art in DRL navigation (57% less collisions in dynamic environments) while reducing the amount of range data processed by 87%, and achieving a 2.6X improvement in processing time compared to classical methods. Focus bug is the first method to achieve the high success rate of robust methods (97%) within such a tight compute budget.

3D shape completion has broad applications in robotics, digital twin reconstruction, and extended reality (XR). Although recent advances in 3D object and scene completion have achieved impressive results, existing methods lack 3D consistency, are computationally expensive, and struggle to capture sharp object boundaries. Our work (RaySt3R) addresses these limitations by recasting 3D shape completion as a novel view synthesis problem. Specifically, given a single RGB-D image, and a novel viewpoint (encoded as a collection of query rays), we train a feedforward transformer to predict depth maps, object masks, and per-pixel confidence scores for those query rays. RaySt3R fuses these predictions across multiple query views to reconstruct complete 3D shapes. We evaluate RaySt3R on synthetic and real-world datasets, and observe it achieves state-of-the-art performance, outperforming the baselines on all datasets by up to 44% in 3D chamfer distance.

Transparent objects are ubiquitous in industry, pharmaceuticals, and households. Grasping and manipulating these objects is a significant challenge for robots. Existing methods have difficulty reconstructing complete depth maps for challenging transparent objects, leaving holes in the depth reconstruction. Recent work has shown neural radiance fields (NeRFs) work well for depth perception in scenes with transparent objects, and these depth maps can be used to grasp transparent objects with high accuracy. NeRF-based depth reconstruction can still struggle with especially challenging transparent objects and lighting conditions. In this work, we propose Residual-NeRF, a method to improve depth perception and training speed for transparent objects. Robots often operate in the same area, such as a kitchen. By first learning a background NeRF of the scene without transparent objects to be manipulated, we reduce the ambiguity faced by learning the changes with the new object. We propose training two additional networks: a residual NeRF learns to infer residual RGB values and densities, and a Mixnet learns how to combine background and residual NeRFs. We contribute synthetic and real experiments that suggest Residual-NeRF improves depth perception of transparent objects. The results on synthetic data suggest Residual-NeRF outperforms the baselines with a 46.1 % lower RMSE and a 29.5 % lower MAE. Real-world qualitative experiments suggest Residual-NeRF leads to more robust depth maps with less noise and fewer holes. Website: https://residual-nerf.github.io

Gas Source Localization (GSL) is a challenging field of research within the robotics community, with high-stakes search-and-rescue applications. Existing methods vary widely and each has its strengths and weaknesses. Comparisons of different methods are limited due to the lack of a broadly adopted and standardized testing methodology. Existing GSL evaluations vary in environment size, wind conditions, and gas simulation fidelity. They also lack photo-realistic rendering for the integration of obstacle avoidance. In this paper, we propose GSL-Bench, a benchmarking tool that can evaluate the performance of existing GSL algorithms. GSL-Bench features high-fidelity graphics and gas simulation, featuring NVIDIA’s®Isaac Sim and OpenFOAM computational fluid dynamics software (CFD). Realism is further increased by simulating relevant gas and wind sensors. Scene generation is simplified with the introduction of AutoGDM+, capable of procedural environment generation, CFD and particle-based gas dispersion simulation. To illustrate GSL-Bench’s capabilities, three algorithms are compared in six warehouse settings of increasing complexity: E. Coli, dung beetle, and a random walker. Our results demonstrate GSL-Bench’s ability to provide valuable insights into algorithm performance.Site: https://sites.google.com/view/gslbench/

We present fully autonomous source seeking onboard a highly constrained nano quadcopter, by contributing application-specific system and observation feature design to enable inference of a deep-RL policy onboard a nano quadcopter. Our deep-RL algorithm finds a high-performance solution to a challenging problem, even in presence of high noise levels and generalizes across real and simulation environments with different obstacle configurations. We verify our approach with simulation and in-field testing on a Bitcraze CrazyFlie using only the cheap and ubiquitous Cortex-M4 microcontroller unit. The results show that by end-to-end application-specific system design, our contribution consumes almost three times less additional power, as compared to a competitive learning-based navigation approach onboard a nano quadcopter. Thanks to our observation space, which we carefully design within the resource constraints, our solution achieves a 94% success rate in cluttered and randomized test environments, as compared to the previously achieved 80%. We also compare our strategy to a simple finite state machine (FSM), geared towards efficient exploration, and demonstrate that our policy is more robust and resilient at obstacle avoidance as well as up to 70% more efficient in source seeking. To this end, we contribute a cheap and lightweight end- to-end tiny robot learning (tinyRL) solution, running onboard a nano quadcopter, that proves to be robust and efficient in a challenging task.

Nano quadcopters are ideal for gas source localization (GSL) as they are safe, agile and inexpensive. However, their extremely restricted sensors and computational resources make GSL a daunting challenge. We propose a novel bug algorithm named ‘Sniffy Bug', which allows a fully autonomous swarm of gas-seeking nano quadcopters to localize a gas source in unknown, cluttered, and GPS-denied environments. The computationally efficient, mapless algorithm foresees in the avoidance of obstacles and other swarm members, while pursuing desired waypoints. The waypoints are first set for exploration, and, when a single swarm member has sensed the gas, by a particle swarm optimization-based (PSO) procedure. We evolve all the parameters of the bug (and PSO) algorithm using our novel simulation pipeline, ‘AutoGDM'. It builds on and expands open source tools in order to enable fully automated end-to-end environment generation and gas dispersion modeling, allowing for learning in simulation. Flight tests show that Sniffy Bug with evolved parameters outperforms manually selected parameters in cluttered, real-world environments. Videos: https://bit.ly/37MmtdL

Autonomous and mobile cyber-physical machines are becoming an inevitable part of our future. In particular, Micro Aerial Vehicles (MAVs) have seen a resurgence in activity. With multiple use cases, such as surveillance, search and rescue, package delivery, and more, these unmanned aerial systems are on the cusp of demonstrating their full potential. Despite such promises, these systems face many challenges, one of the most prominent of which is their low endurance caused by their limited onboard energy. Since the success of a mission depends on whether the drone can finish it within such duration and before it runs out of battery, improving both the time and energy associated with the mission are of high importance. Such improvements have traditionally been arrived at through the use of better algorithms. But our premise is that more powerful and efficient onboard compute can also address the problem. In this article, we investigate how the compute subsystem, in a cyber-physical mobile machine such as a Micro Aerial Vehicle, can impact mission time (time to complete a mission) and energy. Specifically, we pose the question as what is the role of computing for cyber-physical mobile robots? We show that compute and motion are tightly intertwined, and as such a close examination of cyber and physical processes and their impact on one another is necessary. We show different “impact paths” through which compute impacts mission metrics and examine them using a combination of analytical models, simulation, and micro and end-to-end benchmarking. To enable similar studies, we open sourced MAVBench , our tool-set, which consists of (1) a closed-loop real-time feedback simulator and (2) an end-to-end benchmark suite composed of state-of-the-art kernels. By combining MAVBench, analytical modeling, and an understanding of various compute impacts, we show up to 2X and 1.8X improvements for mission time and mission energy for two optimization case studies, respectively. Our investigations, as well as our optimizations, show that cyber-physical co-design, a methodology with which both the cyber and physical processes/quantities of the robot are developed with consideration of one another, similar to hardware-software co-design, is necessary for arriving at the design of the optimal robot.