  To safely deploy legged robots in the real world it is necessary to provide
them with the ability to reliably detect unexpected contacts and accurately
estimate the corresponding contact force. In this paper, we propose a collision
detection and identification pipeline for a quadrupedal manipulator. We first
introduce an approach to estimate the collision time span based on band-pass
filtering and show that this information is key for obtaining accurate
collision force estimates. We then improve the accuracy of the identified force
magnitude by compensating for model inaccuracies, unmodeled loads, and any
other potential source of quasi-static disturbances acting on the robot. We
validate our framework with extensive hardware experiments in various
scenarios, including trotting and additional unmodeled load on the robot.
