  Autonomous exploration of unknown environments with aerial vehicles remains a
challenge, especially in perceptually degraded conditions. Dust, fog, or a lack
of visual or LiDAR-based features results in severe difficulties for state
estimation algorithms, which failure can be catastrophic. In this work, we show
that it is indeed possible to navigate in such conditions without any
exteroceptive sensing by exploiting collisions instead of treating them as
constraints. To this end, we present a novel contact-based inertial odometry
(CIO) algorithm: it uses estimated external forces with the environment to
detect collisions and generate pseudo-measurements of the robot velocity,
enabling autonomous flight. To fully exploit this method, we first perform
modeling of a hybrid ground and aerial vehicle which can withstand collisions
at moderate speeds, for which we develop an external wrench estimation
algorithm. Then, we present our CIO algorithm and develop a reactive planner
and control law which encourage exploration by bouncing off obstacles. All
components of this framework are validated in hardware experiments and we
demonstrate that a quadrotor can traverse a cluttered environment using an IMU
only. This work can be used on drones to recover from visual inertial odometry
failure or on micro-drones that do not have the payload capacity to carry
cameras, LiDARs or powerful computers.
